Arts, Faculty of
Political Science, Department of
DSpace
UBCV
Zhumatova, Saltanat
Doctor of Philosophy - PhD
University of British Columbia
My dissertation examines a recent policy trend in immigrant integration: ‘mainstreaming’, an effort to promote equality between immigrants and EU nationals in generic policy domains such as education, housing, and employment through strategies that address the needs of the population at large. Despite its prominence as a policy approach within the European Union, there is little cross-national comparative work on policy mainstreaming. My dissertation seeks to fill this gap by addressing cross-country variation in the mainstreaming of labour market support, and its impact on the labour market integration of immigrants. I develop a Policy Index of Mainstreaming in Labour Market Support that covers data from 2006 to 2016 across 25 EU/EEA countries. The MLMS Index is the first of its kind to categorize diversity in mainstreamed employment support policy. It measures the scope of mainstreaming and the level of access to mainstreamed policies for various groups of immigrants. I argue that due to its focus on equality, policy mainstreaming in labour market support enhances the convergence between immigrants and EU nationals with reference to employment outcomes and it improves, on average, career prospects for immigrants. Under more robust levels of mainstreaming, the link between immigrant status and labour market performance is reinforced and higher policy exposure is likely to lead to better employment outcomes. I use multilevel Bayesian regression analysis in combination with single-level logistic regression and cross-tabulation analysis to test the policy effects on the employment outcomes of immigrants in the EU. Individual-level data are drawn from the European Union Labour Force Survey. Country-level data are drawn from my Index of Mainstreaming in Labour Market Support, the CIVIX, the OECD and Eurostat. My research allows for an exploration of how mainstreamed labour market integration policies vary over time and place. Next, my work offers new insights into whether mainstreaming is an effective policy strategy. Finally, the dissertation joins a small but growing literature that uses multilevel regression analysis to test integration policy effects. I consider policy context as a community characteristic, which implies that analysis of the impact of mainstreaming should rely on both individual-level and country-level attributes.
MAINSTREAMING THE LABOUR MARKET INTEGRATION OF IMMIGRANTS IN THE EU: POLICY FRAMEWORK AND POLICY IMPACT by SALTANAT (SALTA) ZHUMATOVA Specialist Degree (Honours), Moscow State University, 2002 Candidate of Sciences, Moscow State University, 2005 M.A., Munk School of Global Affairs, University of Toronto, 2012 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postgraduate Studies (Political Science) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) November 2022 © Saltanat (Salta) Zhumatova ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Mainstreaming the Labour Market Integration of Immigrants in the EU: Policy Framework and Policy Impact submitted by Saltanat (Salta) Zhumatova in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Political Science Examining Committee: Antje Ellermann, Professor, Political Science, UBC Supervisor Richard Johnston, Professor, Political Science, UBC Supervisory Committee Member Kai Ostwald, Associate Professor, Political Science, UBC Supervisory Committee Member Matthew Wright, Associate Professor, Political Science, UBC University Examiner Rima Wilkes, Professor, Sociology, UBC University Examiner iii Abstract My dissertation examines a recent policy trend in immigrant integration: ‘mainstreaming’, an effort to promote equality between immigrants and EU nationals in generic policy domains such as education, housing, and employment through strategies that address the needs of the population at large. Despite its prominence as a policy approach within the European Union, there is little cross-national comparative work on policy mainstreaming. My dissertation seeks to fill this gap by addressing cross-country variation in the mainstreaming of labour market support, and its impact on the labour market integration of immigrants. I develop a Policy Index of Mainstreaming in Labour Market Support that covers data from 2006 to 2016 across 25 EU/EEA countries. The MLMS Index is the first of its kind to categorize diversity in mainstreamed employment support policy. It measures the scope of mainstreaming and the level of access to mainstreamed policies for various groups of immigrants. I argue that due to its focus on equality, policy mainstreaming in labour market support enhances the convergence between immigrants and EU nationals with reference to employment outcomes and it improves, on average, career prospects for immigrants. Under more robust levels of mainstreaming, the link between immigrant status and labour market performance is reinforced and higher policy exposure is likely to lead to better employment outcomes. I use multilevel Bayesian regression analysis in combination with single-level logistic regression and cross-tabulation analysis to test the policy effects on the employment outcomes of immigrants in the EU. Individual-level data are drawn from the European Union Labour Force Survey. Country-level data are drawn from my Index of Mainstreaming in Labour Market Support, the CIVIX, the OECD and Eurostat. iv My research allows for an exploration of how mainstreamed labour market integration policies vary over time and place. Next, my work offers new insights into whether mainstreaming is an effective policy strategy. Finally, the dissertation joins a small but growing literature that uses multilevel regression analysis to test integration policy effects. I consider policy context as a community characteristic, which implies that analysis of the impact of mainstreaming should rely on both individual-level and country-level attributes. v Lay Summary Mainstreaming is the practice of centering attention to the needs of vulnerable groups such as immigrants, in broader policy areas such as health, education, and employment. To measure mainstreaming in EU states, I develop the Policy Index of Mainstreaming in Labour Market Support. It reveals that most European countries have robust eligibility rules for immigrants, have limited spending on employment support, and prioritize active labour market policies. ‘New’ EU member states have poorly developed employment support policies and more restrictive access regulations as compared to ‘old’ EU members. Mainstreaming reinforces the idea that immigrants and majority populations can be equally disadvantaged due to shared vulnerabilities. I use the MLMS Index and other datasets to measure whether this approach translates into greater equality in policy exposure between both groups and in better employment outcomes for immigrants in the EU. My findings suggest that mainstreaming is conducive to finding employment among immigrants. . vi Preface This dissertation is original, unpublished, independent work by the author, Saltanat (Salta) Zhumatova. Permission to access the confidential data in the EU Labour Force Survey 2006 – 2018 was granted by the Eurostat of the European Commission. vii Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ...................................................................................................................................v Preface ............................................................................................................................................ vi Table of Contents .......................................................................................................................... vii List of Tables ................................................................................................................................. ix List of Figures ..................................................................................................................................x List of Abbreviations .................................................................................................................... xii Acknowledgements ...................................................................................................................... xiii Dedication .................................................................................................................................... xvi Chapter 1. Introduction ................................................................................................................1 Research Question and the Argument in Brief ...........................................................................3 Policy Implications .....................................................................................................................5 Integration Debates in the Literature: Findings and Gaps ..........................................................6 Research Design ........................................................................................................................19 Dissertation Structure ................................................................................................................20 Chapter 2. Mainstreaming and Labour Market Integration. Theory ....................................24 The Immigrants as ‘Other’ ........................................................................................................25 Un-Othering: Mainstreaming Instead of Targeting ...................................................................30 Policy Mainstreaming ...............................................................................................................36 The Outcome Variable: Labour Market Integration .................................................................39 Immigrants in the Sample: Who Are They? .............................................................................40 The Effects of Mainstreaming on the Labour Market Integration of Immigrants ....................43 Chapter 3. Research Design. Index Building and Policy Impact Assessment ........................50 Index-Building Strategy ............................................................................................................51 Case Study Analysis: Criteria for Case Selection .....................................................................63 Policy Impact Assessment ........................................................................................................65 Chapter 4. The Index of Mainstreaming in Labour Market Support ....................................74 Labour Market Support in the EU .............................................................................................74 viii Labour Market Support as a Case of Policy Mainstreaming ....................................................77 The Index of Mainstreaming in Labour Market Support ..........................................................79 Mainstreaming, Political Culture, and Alternative Integration Frameworks ............................91 Summary of Findings ................................................................................................................98 Chapter 5. Descriptive Case Studies and Mainstreamed Policies at Work ..........................101 Denmark ..................................................................................................................................101 France ......................................................................................................................................107 Slovakia ...................................................................................................................................114 The Mechanism of Policy Impact: Mainstreamed Policies at Work .......................................118 Summary of Findings ..............................................................................................................125 Chapter 6. Policy Impact: Quantitative Analysis ...................................................................129 Research Question and Hypotheses ........................................................................................129 Estimation Strategies ...............................................................................................................131 Data and Samples ....................................................................................................................139 Cross-Tabulation and Regression Analysis ............................................................................146 Summary of Findings ..............................................................................................................170 Chapter 7. Conclusion ...............................................................................................................172 Summary of Findings ..............................................................................................................172 Contributions ...........................................................................................................................177 Policy Implications .................................................................................................................180 References ....................................................................................................................................183 Appendices ...................................................................................................................................198 Appendix A. Tables ................................................................................................................198 Appendix B. Figures ...............................................................................................................251 ix List of Tables Table 1. The Distribution of EU Residents (LFS-2018) ................................................................42 Table 2. Levels of Education by Categories of EU Residents (LFS-2018) ...................................43 Table 3. The MLMS Index. Dimensions and Categories ..............................................................55 Table 4. Testing Internal Consistency in Policy Scope .................................................................63 Table 5. Labour Market Support: Types of Action ........................................................................75 Table 6. Access to Labour Market Support in the EU and Norway ..............................................81 Table 7. Cross-Tabulation of Access and Scope in Mainstreaming ..............................................88 Table 8. Hypotheses, Estimation Strategies, and Models ............................................................134 Table 9. Summary Statistics. Results from Country-Level Cross-Tabulation Analyses .............156 Table 10. Cross-Level Interaction Model, with ‘Contacting PES’ as a Dependent Variable ......158 Table 11. Cross-Level Interaction Model, with ‘Registration with PES’ as a Dependent Variable ........................................................................................................................................160 Table 12. Cross-Level Interaction Model, with ‘Finding Employment via PES’ as a Dependent Variable ........................................................................................................................................162 Table 13. Random Intercept Model, with ‘Finding Employment via PES’ as a Dependent Variable (for TCNs only) ............................................................................................................................165 x List of Figures Figure 1. The Distribution of Third-Country Nationals by the Place of Origin (LFS-2018) ........42 Figure 2. The Mechanism of the Relationship Between Mainstreaming and Labour Market Outcomes .......................................................................................................................................48 Figure 3. Content Validity Across Two Levels .............................................................................57 Figure 4. Access in Labour Market Support. Policy Change in Europe: 2006 – 2016 ..................86 Figure 5. Access in Labour Market Support. 2016 ........................................................................86 Figure 6. Scope in Labour Market Support. Policy Change in Europe: 2006 – 2016 ..................86 Figure 7. Scope in Labour Market Support. 2016 .........................................................................86 Figure 8. Public Spending on Labour Market Support. 2006 – 2016 ............................................87 Figure 9. Public Spending on Labour Market Support. 2016 ........................................................87 Figure 10. Multiculturalism and the Scope of Mainstreaming, with Denmark .............................95 Figure 11. Multiculturalism and the Scope of Mainstreaming, without Denmark ........................95 Figure 12. Multiculturalism and the Total Score of Mainstreaming, with Denmark ....................95 Figure 13. Multiculturalism and the Total Score of Mainstreaming, without Denmark ...............95 Figure 14. Civic Integration and the Scope of Mainstreaming ......................................................97 Figure 15. Civic Integration and the Total Score of Mainstreaming .............................................97 Figure 16. Support for the Far-Right and the Scope of Mainstreaming ........................................97 Figure 17. Support for the Far-Right and the Total Score of Mainstreaming ................................97 Figure 18. Probability of Registering with PES for Three Categories of EU Residents .............148 Figure 19. Probability of Getting Employment via Involvement in PES Programs for Three Categories of EU Residents .........................................................................................................148 Figure 20. Probability of Contacting PES for EU Nationals and Third-Country Nationals ........149 xi Figure 21. Probability of Registering with PES for EU Nationals and Third-Country Nationals ......................................................................................................................................151 Figure 22. Probability of Getting Employment via Involvement in PES Programs for EU Nationals and Third-Country Nationals .......................................................................................153 Figure 23. Probability of Getting Employment via Registering with PES for Third-Country Nationals ......................................................................................................................................154 Figure 24. Probability of Getting Employment via Registering with PES for EU Nationals ......154 xii List of Abbreviations ALMP CIVIX Active Labour Market Policies The Civic Integration Policy Index EEA The European Economic Area HAZ Health Action Zones LFS The Labour Force Survey LMP Labour Market Policy MCP The Multiculturalism Policy Index MLMS The Index of Mainstreaming in Labour Market Support MIPEX The Migrant Integration Policy Index MLE Maximum Likelihood Estimation PES Public Employment Service TCN Third Country Nationals xiii Acknowledgements The contributions of a large number of people have enabled me to complete my dissertation, starting with the most important source of support: my supervisor, Antje Ellermann. This work would not have been possible without her unwavering support and belief in me. It is not only her professional guidance and insightful feedback but also her patience, kindness, and unconditional encouragement during difficult times that have given me confidence to proceed with my PhD. I am grateful to Antje for always standing by my side during my professional, emotional and financial struggles. Antje has been my best role model as a scientist and mentor. I could not have imagined a better supervisor. I am extremely grateful to Richard Johnston, my dissertation committee member, for furthering my thinking about research methodology and index-building. His valuable and constructive suggestions on the quantitative analysis part of my dissertation steered me through this research. His willingness to give his time and effort to work with me under tight deadlines has been very much appreciated. My sincere gratitude goes to Kai Ostwald, also my PhD committee member, for inspiring me to explore the broad political and social context of mainstreaming and to ‘think bigger’ about immigrant integration in general. His insightful comments and useful critiques and especially his relentless focus on consistency, have improved this study in innumerable ways. I had several excellent mentors who contributed at various points to my methods training. I am especially grateful to Elizabeth Hirsh and John Poe. It was Elizabeth's graduate course on advanced quantitative methods in the UBC's Department of Sociology that sparked my interest in econometrics and influenced the methodological orientation of my research. Without John Poe's brilliant course on multilevel modelling at the ICPSR Summer Program at the University of xiv Michigan, I would not have developed the skills I needed to conduct my research. I am extremely grateful to him for his advice and encouragement and for showing me how to explain complicated subjects in simple terms. I would like to extend my sincere thanks to Andrew Owen and Fred Cutler who introduced me to teaching statistics and continuously employed me as a teaching assistant for their course on quantitative methods. This experience was formative as it significantly contributed to my professional development as a teacher and deepened my interest in research methodology. Early versions of the dissertation chapters were presented at the American Political Science Association and Canadian Political Science Association meetings. My findings were also discussed at the UBC Institute for European Studies Colloquium and at the UBC Centre for Migration Studies meetings. I received valuable feedback from numerous colleagues. I am particularly thankful to Edward Konig, Willem Maas, Phil Triadaphilopoulos, and Matthew Wright. I would like to convey my gratitude to the faculty and staff in the Department of Political Science at UBC for providing a friendly and supportive work environment. I am deeply grateful to Michael Byers for his guidance and insightful conversations that helped me find my research direction. I am indebted to Richard Price for his continuous support, encouragement, and excellent teaching advice. I owe special thanks to Lisa Sundstrom for her mentorship during the early stages of my PhD. Barbara Arneil, Erin Baines, Arjun Chowdhury, Alan Jacobs, Yves Tiberghien, and Mark Warren all exerted a great influence on my development as a researcher through course work, critical questions and helpful comments. I am also grateful to Josephine Calazan for her prompt assistance with the visa-related paperwork I burdened her with almost every year. xv I gratefully acknowledge the generous financial support for my research work provided by the UBC Department of Political Science, the UBC Institute for European Studies and the Faculty of Arts at UBC. I am also grateful to the ESTAT Microdata Access Team of the European Commission for facilitating access to the Labour Force Survey data. One of the best experiences in the PhD program was interactions with other graduate students, many of whom became my friends. I am grateful to Alex Held and Miriam Matejova for many wonderful lunches, heartfelt conversations and fun activities we have done together. Dan Westlake has been a supportive friend, the best person to discuss multiculturalism with and my favourite dance partner. I am thankful to Camille Desmares for encouraging and motivating me during difficult times, and to Sandra Schinnerl for sharing her energy and zest for life. I have enjoyed many useful and entertaining discussions with my friend and colleague Brent Sutton. Eon Joung Lee, Dominique Rumeau, Amanda Vance, Jimin Joy were an important part of the support system that helped me survive in graduate school. A warm thank you to Zarai Toledo Orozco for being my study buddy and keeping me sane in the midst of a pandemic. I am thankful to my friend Michael Arzadon for cheering me up with tarot readings that always promised a successful defence. Last but not least, I owe a debt of gratitude to my family. I am grateful to my friend-turned-sister, Katia Semenova, for her care and support. I cannot thank her enough for always being there for me, through good and bad times. My gratitude goes to Anton Lyubinin who encouraged and sustained me through graduate school and beyond. Thank you for your kindness and patience. Finally, my mother, Karla Smagulova, has always believed in me and inspired me to move forward. I dedicate this work to her. xvi Dedication To my mother, Karla Smagulova 1 Chapter 1. Introduction The topic of immigrant integration remains important on the policy agenda of EU states. Russia’s invasion of Ukraine has produced Europe’s fastest-growing refugee crisis since World War II. It was preceded by a big exodus of refugees resulting from Syria’s civil war. Due to migration from former colonies, mass guestworker recruitment after World War II, the influx of immigrants from former post-Communist countries in the 1990s, many contemporary European states have already had large ethnic minority populations. Eurostat reports that in 2021, the number of people residing in the EU-28 with citizenship of a non-member country was 23.7 million (Migration and migrant population statistics, 2022). The majority of new arrivals originate from countries that suffer from military conflict and social and economic collapse. The perceived social and cultural gap between newcomers and natives as well as the rise of far-right parties which have fueled anti-immigrant sentiment in Europe have led more European citizens to agree that immigrants are a burden to the welfare system and a threat to national culture and values (Tabaud, 2020). The increased movement of refugees into European societies has intensified concerns about how European governments can integrate immigrants without imposing a burden on their societies. Two aspects of immigrant integration have been of particular importance to European policy-makers: the labour market performance of immigrants, and their adoption of host society’s culture and societal norms. Randall Hansen (2012) suggests that the disproportionate focus on the role of Islam in European societies and sociocultural integration obscures the fact that the real integration crisis in Europe is one of immigrant unemployment. In other words, the economic integration of immigrants in Europe has not received the attention it deserves. 2 In OECD European countries, immigrants are disproportionately affected by unemployment. Although over the course of 2019 the unemployment rate of immigrants in the EU decreased due to overall improvements in the economy, COVID-19 has reversed this trend (OECD, 2020). In 2020, the EU employment rate for native-born nationals was 8.3 points higher than the rate recorded for the foreign-born population (Eurostat, 2021). Furthermore, there is much evidence of differential treatment and exclusion affecting immigrant workers in Europe: prejudice in recruitment, higher level of vulnerability to economic restructuring and plant closures, and inferior treatment at work (Adida et al., 2010; Oreopoulos, 2011; Dancygier & Laitin, 2014). In Europe, lesser educated immigrants are strongly overrepresented in occupations that have been identified as essential in response to the COVID-19 pandemic (Fasani, 2020). Many of these jobs are characterized by difficult working conditions, low prestige and low pay. Just as the hurdles to immigrant employment and equal treatment to work are complex, so are the policies aimed at improving immigrant labour market inclusion. Immigrant integration in Europe is a relatively new policy area developed after World War II to address the needs of new immigrant populations. Over time, European integration policies have become more diverse. They vary along a number of dimensions: mandatory ‘civic integration’ measures and voluntary integration plans, strategies targeted at immigrants as a specific group versus more generic policies, and approaches that represent “the poles of difference-friendly ‘multiculturalism’ and ‘universalistic ‘assimilationism” (Joppke, 2007). One of the latest policy trends is the mainstreaming of immigrant integration programs. Mainstreaming is the practice of bringing the needs and priorities of vulnerable or underrepresented groups such as immigrants to the centre of attention in more generic policies (e.g. employment support, healthcare, housing, education). This contrasts with policy 3 targeting by ethnicity or immigrant minority status. While European mainstreamed policies have outreach components dedicated to immigrants, they also address the needs of all – newcomer and native-born - members of society who face similar challenges in the labour market, employment or education. Mainstreaming aims to move away from group-specific policies to problem-based policies. An example of mainstreaming is Adultes-relais, the French policy of recruiting unemployed adults to serve as facilitators in banlieues, socially disenfranchised neighbourhoods, in order to enable access to services, offer educational activities for residents and contribute to conflict resolution. The program aims to increase contacts and interactions at the neighbourhood level while matching young unemployed people with jobs. While Adultes-relais does not target immigrants as a group, since many unemployed suburban young people are of immigrant background they benefit from participating in the program; so do native-born French residents of deprived neighbourhoods. The rationale behind this and similar policies is to address the increased socio-economic diversity within immigrant groups; to find policy alternatives to the long-standing integration approaches that have been perceived by many as ‘failed’ (Brubaker, 2001; Entzinger, 2003; Joppke, 2004; 2007; Scholten et al., 2017); and to find a cost-effective way to address the needs of both immigrant groups and the wider population. Research Question and the Argument in Brief This dissertation seeks to answer the following question: to what extent does policy mainstreaming account for the variation in the labour market integration of immigrants in the EU? I focus on mainstreaming in labour market support because employment is one of the most important indicators of immigrant integration. It determines immigrants’ economic 4 well-being and social standing in the host country and benefits the host-country economy. The question can be addressed in relative and absolute terms. First of all, this is essentially a question of equality between immigrants and natives with ‘native’ employment as the reference point. I measure labour market integration as the difference in labour market outcomes for immigrants and similarly situated natives. Do immigrants receive the same level of exposure to policy mainstreaming in generic policy domains as EU nationals? If so, is the effect of mainstreaming more pronounced for EU nationals than for immigrants? Furthermore, is mainstreaming conducive to better employment outcomes in absolute terms, i.e. are those immigrants who receive policy treatment more likely to be employed than those who do not? I conceptualize mainstreaming as a policy strategy intended to promote equality between immigrants and EU nationals in generic policy domains such as employment support. This is achieved by addressing vulnerabilities and concerns shared by both groups, e.g. physical disability, the lack of education or limited work experience. The labour market integration of immigrants entails economic self-reliance through full-time employment. The term ‘immigrants’ refers to ‘third-country nationals’, i.e. individuals who are not citizens of the European Union and who do not have a right to move or reside freely within the territory of the EU Member states. I use the terms ‘immigrants’ and ‘third-country nationals’ interchangeably. I argue that mainstreaming in labour market support is likely to have a positive impact on the labour market integration of third-country nationals in the EU. Under more robust levels of mainstreaming, both at the level of access and at the level of scope, the link between immigrant status and labour market integration is reinforced and higher policy exposure is likely to lead to better employment outcomes. 5 Due to its focus on multiple aspects of social vulnerability that cut across groups, policy mainstreaming in labour market support is conducive to the economic integration of immigrants in the EU in relative and absolute terms. First, it enhances the convergence between immigrants and EU nationals with reference to employment outcomes; and second, it improves, on average, career prospects for immigrants. The proposed correlation mechanism unfolds as follows. More generous state funding of mainstreaming in labour market support is likely to lead to the development of more expansive and better functioning programs. As a result, they become more appealing to third-country nationals. This, in turn, enhances policy exposure, i.e. formal enrollment in such programs. Full enrollment is crucial since certain employment support programs are only available to jobseekers who are formally registered with national public employment services. Under such enrollment, on average, we expect to observe better employment outcomes among third-country nationals in the EU. Policy Implications The question of how variation in mainstreaming affects immigrant integration has important policy implications. It helps us understand which strands of integration policy have been effective in the adaptation of newcomers across EU countries. It offers policymakers a new framework, which can help design applicable long-term solutions for the improvement of integration programs. It also contributes to major policy debates such as whether or not the ‘liberal’, ‘non-repressive’ approaches to immigrant integration that do not impose any requirements or obligations on immigrants have proved successful. Today, when Europe’s values are being challenged by growing right-wing populism, it is important that some of the most burning questions in the EU policy agenda are not left unanswered. 6 Integration Debates in the Literature: Findings and Gaps My dissertation addresses some major gaps in the academic debate on the labour market integration of immigrants. Scholarship on this topic draws on a range of disciplines including sociology, economics, and political science. The political science literature focuses on immigrant integration policies and their theoretical frameworks (e.g. multiculturalism, civic integration, mainstreaming). Studies in economics and sociology examine non-policy determinants of immigrant integration such as human capital, discrimination, and social networks. Political scientists tend to study integration policies as an outcome; there are very few works to date that explore their effects. Studies in economics and sociology, on the other hand, focus on the effects of non-policy determinants on immigrant integration. The integration outcome these studies seek to measure is the attainment of financial independence through full-time employment. The next section contains an overview of causal factors that have been posited as main drivers of immigrants’ labour market integration. The section also identifies major integration debates and gaps in the literature. Non-Policy Determinants of Labour Market Integration Human Capital. Outside of political science, there are two major theoretical approaches that identify the non-policy determinants of the labour market integration of immigrants: the theory of human capital developed by labour economists Gary S. Becker (1964) and George Borjas (1987, 1988), and the theory of discrimination elaborated in economics and sociology. Economists use the discrepancy in earnings between native-born citizens and immigrants as a measure of economic integration. Sociologists focus on the social capital of immigrants. 7 According to human capital theory, immigrants’ labour market performance is determined by their human capital, i.e. social and personal attributes such as educational attainment, work experience, knowledge of the host country language and other skills. These attributes can be of two types, observable (e.g. proficiency in the host country’s language, education level) and unobservable (e.g. talents and abilities). It is assumed that advanced education and skills are associated with higher labour market performance. Countries such as Canada and Australia have adopted a ‘positive’ pattern of migration (since the move to a points-based system in the 1960s-1970s) with immigrants selected for their observable skills. Other countries, many of them European, have a ‘negative’ model of selection, i.e. accept immigrants who are in need of state-provided protection and are likely to have lower human capital attributes (e.g. refugees). George Borjas (1988) argues that countries that favour a ‘positive’ pattern of migration tend to have higher immigrant integration rates than states with a ‘negative’ model. Home country human capital, however, is not always transferrable. To be competitive in the host country labour market, immigrants often have to either improve their skills or acquire new country-specific skills – learn a new language, or build networkers with native-born citizens (Bevelander & Scott, 1996; Duleep & Regets, 1999; Chiswick & Miller, 2009; Duleep, Liu, & Regets, 2014). According to Chiswick (1978), immigrants’ occupational mobility is based on a U-shaped pattern: due to the suboptimal transferability of skills across countries, immigrants, initially, experience a decline in occupational status upon arriving in a new country. Over time, as they invest in human capital, their occupational attainment and wages increase. Labour market integration patterns of immigrants differ depending on their initial motivation; economic immigrants, on average, experience smaller employment 8 penalties than asylum seekers and family immigrants – those with low host country human capital (Zwysen, 2019). Discrimination. Critics of human capital theory challenge the notion that employers are rational actors motivated by utility maximization. They argue that the labour market is not a neutral arena that always favours workers with higher social attributes, regardless of their race and gender. One alternative to human capital theory was a dual labour-market theory developed in the 1960-1970s, in The Dual Economy by Richard Averitt (1968) and Internal Labour Markets by Peter Doeringer and Michael Piore (1971) and later re-labeled as ‘segmentation theory’. It is predicated on the assumption that labour market is segmented and that certain social groups are disadvantaged and discriminated against. Most core well-paid jobs are acquired by men who belong to mainstream cultural groups; women, visible minorities and recent immigrants form the secondary segment and end up in low-status employment. For example, Susan Hanson and Geraldine Pratt (1995) show how the kin and friendship networks that male workers activate in finding employment segregate women. There are two mechanisms of labour market discrimination. The first one, ‘taste for discrimination’ or preference-based discrimination, is based on the seminal study The Economics of Discrimination by Gary S. Becker (1957): employers act on their prejudice against certain racial and ethnic groups – for example, they choose not to hire, work with or buy from black people. If the number of such employers is large, the earnings of minorities will be lower than the earnings of workers with similar skills from majority group. The second mechanism, ‘the theory of statistical discrimination’, was pioneered by Kenneth Arrow (1985) and Edmund Phelps (1972). It is based on the assumption that profit-maximizing employers make decisions based on race or sex group averages on indicators of productivity. When employers are unable to obtain reliable information about the individual 9 or the information is costly, they often rely on the information about the average productivity of the social group such as immigrants or women. For example, employers or college admission officers may devalue education or work experience obtained in a foreign country and treat individuals with similar credentials differently, depending on what country the education or work experience was received. Alternatively, employers may choose not to invest in the human capital development of women (even if they are equally qualified as men) because they have learned from past experience that women workers are more likely to reduce work hours. Employers base their decisions on negative ethnic stereotypes – for example, as Philip Oreopoulos demonstrates, resumes with English-sounding names are significantly more likely to receive callbacks than resumes with Indian and Chinese names because employers treat a foreign names as a signal that an applicant may lack critical language or social skills for the job (Oreopoulos, 2011)1. Sometimes, the key causal factor is spillover discrimination – when discrimination in one area of society leads to negative effects in other realms. For example, discrimination in the school system creates problems for immigrants in the labour market; one form of spillover discrimination, as in the case of Sweden, is network recruitment: employers tend to hire someone who belongs to the same network (Rydgren, 2004). The economic literature does not consider policy to be the solution to labour market discrimination. Instead, labour economists suggest that discriminating employers are sooner or later penalized by market forces, which is why discrimination cannot exist in the long run. For example, Becker’s theory states that ‘taste-based’ discrimination is costly: employers who discriminate less have lower labour costs and can therefore make higher profits. 1 Similar studies in France suggest that a Muslim applicant is 2.5 times less likely to receive a job interview callback than is his or her Christian counterpart (Adida et al., 2010). 10 Similarly, a company that is able and willing to test their job applicants on relevant characteristics will do better than a company that relies on averages. Social Capital. Sociological research indicates that social capital is an important causal factor in immigrants’ employment success. Social capital refers to social networks or relations which are based on norms of trust, reciprocity and cooperation (Bourdieu, 1994; Castles & Miller 2009; Putnam et al., 1993; Putnam, 2007). Social capital can be classified as bonding, bridging or linking. Bonding social capital refers to horizontal ties between individuals of the same social or ethnic group; it is often associated with local communities such as families and ethnic neighbourhoods where many people know each other. Bridging social capital refers to horizontal ties between social groups which are not alike demographically but might have similar social and financial status. Linking social capital refers to vertical ties between groups and people in positions of authority and influence (government agencies, political parties). It is believed that a homogenenous social network (bonding) negatively affects immigrants’ economic performance; whereas a more heterogeneous social network (bridging and linking) leads to better labour market outcomes among immigrants. For example, Borjas (1994; 1995) finds that living in ethnic neighbourhoods has a negative impact on the economic performance of immigrants in the US. Casey Warman (2005) gets similar results while assessing the impact of enclaves on the ten-year wage growth of immigrants to Canada. Putnam (2000) suggests that overreliance on networks may reinforce immigrants’ dependency on niche markets. The process of network formation within the same ethnic group can be problematic because overreliance on bonding has to be reconciled with competition and wariness, as in the case of Polish immigrants in London: the Poles who relied most on their co-ethnics for employment and accommodation, were also the most distrustful of their fellow Poles (Ryan et al., 2008). 11 However, as other scholars argue, networks are not static but temporally and spatially dispersed; they can change and expand over time, as newly arrived immigrants become more familiar with the host country environment. Some studies suggest that immigrants who are able to establish strong trusting relationships with their co-ethnics and who are proficient in the host country language, can adapt these skills to establish ties outside their ethnic community; in other words, extensive bonding is linked to bridging (Cox, 2000). Policy Determinants of Labour Market Integration Multiculturalism Policies. In political science, one of the main points of contention has been whether current integration policies facilitate or hinder the labour market performance of immigrants. Concerns over multiculturalism feature prominently in the integration policy debate. Critics of multiculturalism argue that the special acknowledgement of cultural differences within a dominant culture impedes the acquisition of host country language and ‘soft skills’, which in turn, negatively affects the employment prospects of immigrants. Proponents of multiculturalism argue that greater recognition of cultural differences, on the contrary, facilitates labour market integration and contributes to the development of shared civic identity (Kymlicka, 2010). Until recently, the academic literature on multiculturalism has been dominated by political theorists and has focused on multiculturalism as a discourse rather than public policy (Honneth, 1996; Taylor, 1992; Barry, 2002; Kymlicka, 1995). The creation of three datasets, MIPEX (Migrant Integration Policy Index); the Multiculturalism Policy Index (Banting et al., 2006), and the Indicators of Citizenship Rights for Immigrants database (Koopmans & Michalowski, 2017) have moved the topic of multiculturalism from normative debate to the area of empirical research. However, there have been relatively few studies that discuss the 12 impact of multicultural policies on integration outcomes. Irene Bloemraad and Matthew Wright (2014) believe that this is in part due to data constraints: “until recently, few surveys collected large immigrant-origin samples, and administrative data with information on ethno-racial origins are rare or restricted” (p. 304)2. Another challenge is disentangling the effects of multiculturalism from those of welfare policy, citizenship regimes, antidiscrimination policy and other contextual predictors which can be empirically correlated with multicultural policies (Koopmans, 2013; Bloemraad & Wright, 2014). The effects of multiculturalism on levels of public trust and sociocultural integration have been examined with opposing results: some studies suggest that the relationship is negative (Agirdag et al., 2011; Wright, 2011); others report increase in social trust (Kesler & Bloemraad, 2010) and decrease in anti-minority prejudice (Weldon, 2006). In terms of labour migrant integration outcomes, scholars have focused on two factors as conducive to obtaining employment: host country language acquisition and interethnic contacts. Host Country Language. Language acquisition is argued to be a key factor that facilitates immigrants’ employment in the host country. Several studies have examined the contextual determinants of the “origin effect” and “the destination effect”, i.e. the impact of home country and host country characteristics on immigrants’ language acquisition. Van Tubergen & Kalmijn (2005), focus on the role of governing parties in the host country as a destination effect. In their analysis of more than 186, 000 immigrants from multiple origin groups, in nine destinations including EU countries and the US, Van Tubergen & Kalmijn have found that in states with a left-wing legacy, immigrants have poorer command of the 2 The surveys that contain large enough immigrant-origin samples were fielded in the early 2000s (Bloemraad and Wright 2014). 13 host country language since left-wing governments tend to favour a multicultural policy of linguistic pluralism, which eliminates immigrants’ incentives to learn the host country language. Chiswick and Miller (1999) have found a similar effect while analyzing immigration integration in Australia. They developed a theoretical model of dominant-language fluency among immigrants, where fluency is a function of different factors including exposure to the destination language. This exposure, in turn, includes the extent of post-migration training in the destination language. Chiswick and Miller argue that the introduction of multiculturalism – specifically, of mother-tongue education in Australian schools – has reduced immigrants’ exposure to the English language and has led to decline in English language proficiency among immigrants. Evelyn Ersanilli and Ruud Koopmans (2011) provide evidence that supports these findings. They analyze integration outcomes among Turkish immigrants in France, Germany and the Netherlands and find that Turks in non-multicultural France demonstrate a higher level of host language proficiency than Turks in the Netherlands and Germany, countries that grant immigrants a relatively high extent of accommodation of cultural diversity. Interethnic Contacts. The backlash against multiculturalism involves specific “idioms of condemnation” (Vertovec & Wessendorf, 2010), one of which is ethnic segregation or ethnic separatism. Empirical research on multiculturalism examines whether it creates boundaries between ethnic groups which lead to the elimination of social contacts. Is this claim valid? Cross-national studies of immigrant interethnic contacts are scarce. Ersanilli and Koopmans (2011) argue that Turkish immigrants in France have more extensive contacts with majority society that their counterparts in the Netherlands and Germany, which is in line 14 with her argument on the impact of multiculturalism on immigrants’ language acquisition. Koopmans’ cross-national analysis of the relationship between integration policies and welfare regimes in eight EU countries suggests that multicultural policies, when combined with a generous welfare state, have produced high levels of social segregation (Koopmans 2013). Sweden, Belgium and the Netherlands which score high on the multiculturalism index and have extensive welfare policies, display poor integration outcomes as compared to more assimilationist states, such as Germany, Switzerland, Austria and France, or a relatively lean welfare state – the UK (2010). Musterd’s assessment of the levels of social segregation in European cities displays similar results (2005). Immigrant Employment. So far, the evidence for the direct impact of multicultural policies on immigrants’ labour market performance has been inconclusive: “it is debatable how much multiculturalism affects outcomes such as jobs, poverty, and criminal activity net of other factors. Antidiscrimination policies’ employment protections may be of greater importance”. (Bloemraad & Wright, 2014; p. 321). Similarly, Van Tubergen (2004; 2006) does not find a difference in unemployment and labour force participation rates of first generation immigrants between classic immigrant-receiving countries (Canada, Australia) and EU states, despite variation in multiculturalism policy development, once “the origin effect” (GDP, religion, political regime) is taken into account. Some studies examine the combined impact of multicultural policies and welfare policies on immigrant integration outcomes. Scholarly interest in this particular policy combination stems from the idea of a “progressive dilemma” (Kymlicka & Banting, 2006): social democrats are faced with a trade-off between economic redistribution and cultural recognition, i.e. between sustaining their traditional agenda of social equality and embracing cultural diversity. Some critics of multiculturalism believe that ‘group-differentiated rights’ 15 granted to minorities may lower mutual trust among citizens and reduce public support for welfare programs. Banting et al. (2006) argue that these fears are overstated and there is no evidence that multicultural policies indeed erode the welfare state. Ruud Koopmans looks at how multicultural policies and welfare-state regimes have affected the socioeconomic integration of immigrants in European countries (Koopmans, 2010). His analysis suggests that multicultural policies have produced low levels of labour market participation, a finding which, he argues, is a result of easy access to equal rights, the absence of incentives for host-country language acquisition, and a generous welfare state. Policies that emphasize linguistic and cultural assimilation exert pressure on immigrants to acquire skills, knowledge, and social ties that improve their chances on the labour market (Germany, Austria, Switzerland, France). By contrast, multicultural policies that emphasize immigrants’ own language and culture and stimulate immigrants to orient themselves towards their minority group, may have the unintended consequences of sustaining linguistic deficiencies and a lack of cultural ‘soft skills’ (Sweden, Belgium, the Netherlands). This, he argues, makes immigrants non-competitive in the labour market. There is some evidence that the combination of extensive welfare systems and strong multicultural policies can lead to lower labour market participation (Koopmans, 2010). For example, Koopmans refers to a set of works, some of which indicate that weak welfare states raise immigrants’ labour market participation rates (Kogan, 2006). However, there are other studies that suggest that welfare state regime has no effect on economic integration of second-generation immigrants (Fleischmann & Dronkers, 2010). All these works neglect the role of multicultural policies (Koopmans, 2013). Civic Integration Policies. The most significant cross-national change in integration policy in the European Union is that of policy convergence. According to Christian Joppke 16 (2007), the 2004 European Council agreement on ‘Common Basic Principles’ of civic integration policy is an articulation of a “seismic” shift towards greater assimilation in policy paradigms of European states. Academic literature on the impact of civic integration policies is rather scarce. As Sara Wallace Goodman and Matthew Wright tell us, “despite the widespread nature of this policy, its high political visibility and policy relevance, policy-makers and academics still know very little about its effects” (Goodman & Wright. 2015; p. 1887). Most studies look at the relationship between civic integration and existing citizenship policies, ideological orientation of government or backlash against multiculturalism (Goodman, 2013; Joppke, 2007; Onasch, 2017). Some studies argue that there is a link between civic integration policies and reductions in family immigration and labour immigration across Europe (Ahlén & Boräng, 2018). Another study argues that CIP have led to a centralization of integration policy and diminished role of cities (Gerhardt, 2015). Goodman and Wright (2015) have found little evidence that socioeconomic immigrant integration (‘subjective financial well-being’, employment status and social trust) is affected by civic integration policies, either positively or negatively. Civic integration policies tend to have a modest positive effect on political integration measured by immigrants’ political interest, perception of politics as complicated or not, and difficulty in making political decision; all of this tends to increase in high CIVIX countries. The authors conclude that civic integration is “more politically strategic and rhetorically popular (particularly with a public hostile to immigration) than it is functional and effective as an integration policy” (Goodman & Wright, 2015; p. 1902). Mainstreaming in Immigrant Integration This section explains what mainstreaming in immigrant integration entails. Similarly 17 to multiculturalism, mainstreaming has a range of meanings; it is conceptualized as a policy strategy, type of public discourse and a governance shift from state-centric ways of developing policies that accommodate vulnerable groups to poly-centric policy coordination. Until recently, mainstreaming has been limited to areas such as gender, disability, and environment (Nunan et al., 2012; Verloo, 2005; Walby, 2005; Seddon, 2001). Immigrant integration, by contrast, is a more recent application of policy mainstreaming. The academic literature focuses on mainstreaming predominantly as a new type of governance; it discusses theory debates focused on the concept itself (Scholten et al., 2016, Scholten, 2017) and on constructions of target groups (Pierce et al., 2014). The literature on policy implementation is mostly based on case studies conducted by Migration Policy Institute Europe in major European countries such as Denmark (Jørgensen, 2014), Germany (Bendel, 2014), France (Escafré-Dublet, 2014), the UK (Ali & Gidley, 2014; Collet & Petrovic, 2014). The case studies examine national approaches to policy mainstreaming; factors that contributed or obstructed the mainstreaming of integration governance; and policy implementation in small target groups (e.g. young immigrants and descendants of immigrants in France). So far, no cross-country comparisons of policy strategies or policy effects have been conducted. This dissertation is intended to address these gaps. Addressing the Gaps in the Literature: Contributions of the Dissertation My work makes four central contributions to the literature on immigrant integration First, I create an original policy index based on data from the Eurostat Labour Market Policy database, the MIPEX, and administrative sources in each EU country. Building on data from the Index, I have identified major policy patterns across the EU. Mainstreamed policies vary in terms of scope (types of policies) and levels of accessibility for various groups of 18 immigrants. The MLMS Index is the first attempt to examine and categorize diversity in mainstreamed policy strategies. Second, my work examines the question of whether mainstreaming in labour market support is an effective immigrant integration strategy. To answer this question, I provide a cross-country comparison of policy effects. I examine whether third-country nationals have the same level of exposure to policy mainstreaming in labour market support as EU nationals; whether policy impact is stronger for immigrants as compared to non-immigrants; and whether those immigrants who were involved in mainstreamed programs exhibit better results than immigrants who did not receive policy treatment. Since mainstreaming in immigrant integration is a relatively new policy strategy in the EU, it has received relatively little scholarly attention. Immigrant integration studies have focused predominantly on assessing the policy impact of multiculturalism, civic integration and welfare policies. The academic literature on mainstreaming is mostly focused on mainstreaming as a type of governance and a new concept in theoretical debates on immigration. There have been few attempts to evaluate the impact of policy mainstreaming on the labour market performance of third-country nationals, and they rarely go beyond single case study analyses. This study, in contrast, provides a cross-national analysis of the impact of policy mainstreaming. Third, my study uses multilevel Bayesian regression analysis in combination with single-level regression to estimate policy effects. While multilevel modelling has been effectively used in policy analysis to accommodate two-level data structure with individuals nested in groups (e.g. countries), the use of Bayesian framework instead of more conventional maximum likelihood ‘frequentist’ approach is far less common. This methodological strategy is relevant when we need to obtain exact inferences about group- 19 level effects (i.e. parameters, not estimates) and the number of groups (countries) is relatively small. Last but not least, although this dissertation focuses on integration policies, it by no means ignores non-policy dimensions. Human capital factors, e.g. host-country relevant job skills, can be reinforced by relevant integration policies such as mainstreamed apprenticeship courses. The level of discrimination experienced by newcomers is often reflective of the strength of ‘positive action’ policies in the host country; my study covers this aspect of mainstreaming and its impact. Integration policies can facilitate immigrants’ social capital – for example, policies that encourage interaction between majority and minority groups contribute to ‘bridging’ and ‘linking’ social capital. Thus, my research can contribute to scholarly literature outside of political science by explaining how integration policies set up a context for important non-policy determinants. Research Design I use a two-stage methodological approach to test my theory. First, I design and compile the MLMS Index as a strategy intended to empirically capture variation in policy mainstreaming in labour market support across the EU/EEA. Second, I combine policy data obtained from the MLMS Index with individual-level data drawn from the EU Labour Force Survey to examine the relationship between mainstreaming and the labour market outcomes of third-country nationals in the EU. In my analysis, I use a method of cross-validation that employs different statistical techniques to validate the same type of relationship. I use Bayesian multilevel regression analysis to examine if country-level distinctions in mainstreaming have any effect on the labour market outcomes of immigrants. In addition to this, I conduct a single-level logistic regression analysis and cross-tabulation analysis to 20 measure the overall average effect of immigrant exposure to employment support and the average effect of policy mainstreaming on immigrant integration in the labour market across the EU. My methods of analysis are mostly quantitative. They have certain limitations. First, it is well known that regression analysis alone cannot establish causality. Second, it does not offer tools to identify key components that make policy interventions effective or non-effective. This study therefore refrains from making robust causal claims. Instead, it examines a possible correlation between mainstreaming and employment outcomes. To partly mitigate disadvantages stemming from the regression-based strategy, I use qualitative policy analysis and descriptive case study analysis that contextualize findings from the MLMS Index and help identify the mechanism of policy intervention. Dissertation Structure The dissertation is organized as follows. Chapter 2 clarifies the key concepts used in the dissertation and theorizes mainstreaming as a new approach to immigrant integration. It explains how mainstreaming challenges the idea of “the Other” in alternative policy paradigms such as multiculturalism and assimilationism by offering a different perception of immigrants. The chapter provides a brief overview of immigrants’ attributes in the sample. The chapter then outlines the main argument on the relationship between policy mainstreaming and the labour market integration of third-country nationals in the EU. Chapter 3 discusses the background and rationale for the research design used in this study. Following the two-stage methodological strategy, this chapter consists of two sections: one is devoted to index-building, another to policy impact assessment. I elaborate on the methodological foundation of my Index of Mainstreaming in Labour Market Support (the MLMS Index) by describing the sources, the data collection procedure and the validity and 21 reliability of index scores. The second section justifies the need for Bayesian multilevel modeling, cross-tabulation analysis, and single-level logistic regression as the methods employed to measure and verify the relationship between mainstreaming and labour market integration. The final section of the chapter explains my selection strategy for the case study analysis. Chapter 4 introduces the Index of Mainstreaming in Labour Market Support and discusses its two dimensions: the policy scope including the ‘direct’ and ‘indirect’ types of mainstreaming and its accessibility for third-country nationals. It also situates mainstreaming among other integration policy indices (multiculturalism, civic integration) and discusses the association between mainstreaming and political behaviour. The chapter finds that most ‘old’, mostly Western European EU countries have robust eligibility rules for immigrants but limited spending on policies. The EU states tend to prioritize policies that ‘activate’ jobseekers (e.g. training, apprenticeship). ‘New’ EU states, most of them Eastern European, have more poorly developed labour market support policies and restricted access to employment support. The chapter also finds that mainstreaming has little to no correlation with other integration indices and with public support for right-wing parties. Chapter 5 offers a case study analysis by contextualizing mainstreaming in Denmark, France, and Slovakia. It identifies institutional complementarities between mainstreaming and other types of policies and reveals major policy patterns in mainstreaming across the EU based on the findings from the MLMS Index. The final section of the chapter focuses on how mainstreamed policies correlate with employment outcomes by looking at how certain policies work in Denmark and France. 22 The chapter finds that mainstreaming tends to interact with and support dominant patterns in ‘neighbouring’ policy domains such as social security policy – in other words, that it can serve as an instance of institutional complementarity. The case studies also reveal that in all the three EU countries, mainstreaming has not been a radical policy shift that challenged the foundations of the long-established approaches to integration. It could be seen as a new policy tool designed to achieve the old integration goals. The analysis of causal mechanism reveals that despite cross-country differences in policy patterns, the overall trajectory of policy impact in France and Denmark remains the same: both countries are committed to investing in Active Labour Market Policies and both countries use ‘indirect’ and ‘direct’ mainstreaming to make employment support policy more responsive to jobseekers’ needs. Chapter 6 evaluates the impact of policy mainstreaming in employment support on the labour market integration of third-country nationals in the EU and presents main findings. It examines the level of exposure of immigrants to labour market support policies and evaluates the effect of mainstreamed labour market support policies on employment outcomes by examining the link between policy exposure and labour market outcomes. Finally, it discusses which group of EU residents, immigrants or non-immigrants, benefits more from employment assistance. My findings reveal that both third-country nationals and EU nationals prefer to use job search strategies other than relying on state-funded employment support to find a job. However, the demand for labour market support is higher among third-country nationals than among EU nationals as they more often contact Public Employment Services (PES) to find employment. This effect is even higher in countries with higher levels of mainstreaming. Finally, there is a strong positive relationship between formal registration at PES and 23 employment outcomes for third-country nationals which indicates that full enrolment in programs brings better results. It is unclear, however, whether this effect is conditional on the level of mainstreaming in a specific country. In Chapter 7, I integrate my findings into a single conclusion. They are grouped into two categories. The first group of findings is descriptive: it includes cross-national policy patterns in mainstreaming in labour market support revealed by the Index of Mainstreaming in Labour Market Support. The second group of findings examines the impact of mainstreaming on the labour market integration of immigrants in the EU. The chapter addresses the findings along the two categories. The chapter then provides a summary of the dissertation’s contributions and limitations and discusses recommendations for future research. 24 Chapter 2. Mainstreaming and Labour Market Integration. Theory Mainstreaming is a relatively recent approach to immigrant integration. Before we proceed to the question of whether or not this new approach is effective, we need to clarify what makes it conceptually different from long-standing integration policy paradigms. I anchor this discussion in the analysis of varying understandings of the idea of “the Other” in competing integration frameworks. Specifically, I look at how immigrants have been “othered” at the levels of policy and discourse in national models of integration and how mainstreaming challenges these prevailing views by offering a different, non-colonial view of an immigrant which is consistent with the newly emerged awareness of superdiversity – a condition characterized by “the dynamic interplay of variables among an increased number of new, small and scattered, multiple-origin, transnationally connected, socio-economically differentiated and legally stratified immigrants who have arrived over the last decade” (Vertovec, 2007, p. 1024). This new representation of immigrants guides a new policy shift in integration and opens the way to the discussion about the policy impact of mainstreaming. This chapter is organized into four sections. First, I discuss how immigrants have been “othered” by policy-makers and academics through both assimilationism and multiculturalism. Second, I discuss the representation of immigrants in mainstreaming and explain how it correlates with the recent trend in superdiversity. This section is followed by further discussion of mainstreaming (its origins, dimensions, and variation at national and subnational levels) and the outcome variable – the labour market integration of immigrants. The chapter then proceeds by theorizing the impact of mainstreaming on the labour market integration of immigrants. 25 The Immigrants as ‘Other’ Migration scholars commonly posit that most national immigrant integration models vary “between difference-friendly ‘multiculturalism’ and universalistic ‘assimilationism’” (Joppke, 2007b, p. 2). When scholars explain why different countries have followed distinct pathways for integrating immigrants, they typically refer to normative values systems or political philosophies that serve as the basis for cross-national policy variation (Bertossi & Duyvendak, 2012; Brubaker, 1992; Favell, 1998; Schain, 2009). For example, the French assimilationist model of integration is argued to build on a civic conception of national identity (as opposed to ethnocultural national identity as is argued to be the case in Germany). ‘Frenchness’ is argued to indicate commitment to Republican values such as the separation of public and private realms as opposed to having French ancestry. Accordingly, while religion and cultural practices belong to the private sphere, the public realm including public policy must remain secular (the notion of laicite). France’s “colour-blind” integration policy is a manifestation of this approach. By contrast, in ‘multiculturalist’ European states, there is a strong emphasis on the recognition of cultural difference by the state. For example, the main trait of the now-abandoned Dutch multicultural model is the idea that cultural pluralism is the key to the integration of immigrant minorities into Dutch society. ‘Multiculturalist’ integration policies are supposed to promote group-based identities rather than reinforce a common civic identity (Joppke, 2007b; Koopmans e al., 2005). National models of integration, especially the integration outcomes of the “multicultural shift” that occurred in the 1980s, have been highly problematized in academic and policy debates (Barry, 2002; Banting et al., 2006; Joppke, 2007; Koopmans, 2010). In the early 2000s, the relevance of these models was questioned after a series of emblematic events were viewed as instances of integration failure: violent urban riots in France, terrorist attacks 26 in the UK and the murder of Theo Van Gogh by a radical Islamist in the Netherlands (Vertovec & Wessendorf, 2009; Joppke, 2007; 2009). Some scholars have further argued that disparities between national models have become blurred through a process of policy convergence and a shift towards civic integration in Europe (Joppke, 2007, 2009; Klausen, 2005). However, many scholars agree that the national philosophies can be viewed as institutional legacies that feed policies and policy shifts. The key element of a national philosophy that informs integration policy is a nation’s understanding of membership (Goodman, 2014). In my work, I assume that the direction of integration policy can also be influenced by what membership is not – in other words, by how non-members, i.e. immigrants, are portrayed in the national political philosophy underlying immigrant integration. I contend that the representations of immigrants can be as important to the norms systems as the idea of membership since they dictate what policy treatment, if any, non-members are entitled to. While understandings of membership can be persistent as they are deeply rooted in histories of national self-understandings (Brubaker, 1992), perceptions and discursive representations of immigrants tend to be more susceptible to change as they are dependent on the everyday encounters between natives and newcomers resulting in fluctuations in public opinion and policy. Under multiculturalism, immigrants are perceived as vulnerable, disadvantaged minority groups entitled to special protections. This understanding stems from two justifications of multiculturalism, liberal egalitarian and postcolonial. According to liberal egalitarian theory, immigrants are freely choosing agents who deserve identical protections provided by the state (Taylor, 1992). Free choice is impossible if the state imposes a hegemonic culture and identity on everyone (e.g. ‘Anglo-conformity’). In addition to basic civic rights, a multicultural state must therefore provide ‘group-differentiated rights’ and 27 protect the rights of a minority group (or members of such a group) “to act or not to act in a certain way in accordance with their religious obligations or cultural commitments.” (Kymlicka, 1995). Postcolonial justification adds one more dimension to this explanation: immigrant groups have historically been a part of colonial populations suppressed by metropoles which are now major migrant-receiving countries. Multiculturalism is viewed as a way to redress historic injustice and offer an apology to historically oppressed and marginalized groups. It is remarkable that postcolonial discourse has been extended to post-Communist Eastern European states which have served as a major source of immigrant labour to the EU: Kymlicka labels Eastern European immigrants “minoritized majorities” since they “continue to think and act as if they are weak and victimized minorities and which therefore continue to live in existential fear for their existence” (Kymlicka, 2007). In the discourse of multiculturalism, Europeans from the former Soviet bloc are viewed as colonial subjects of another imperial power, the Soviet Union. From this perspective, an immigrant to Europe, is seen as, to use Antonio Gramsci’s terminology, a “subaltern native”: a non-white (or racialized white) Other who has come from a poor peripheral country to a former imperial power (Gramsci, 1971). “The subaltern” is weak, disadvantaged, and powerless – socially, culturally, and economically - with the economic disadvantage stemming from racial discrimination and lack of ‘soft skills’. This lack of power is not caused by immigration status alone; race and ethnicity play a dominant role here since they are viewed as proxies for cultural distinctiveness: ‘immigrant minorities’, not ‘immigrants’, make a separate target category in the Multiculturalism Policy Index by Kymlicka and Banting. Multiculturalism almost neglects privilege produced by social class. A non-white 28 immigrant with high education and high income, regardless of their immigration status, which is often the result of the above (compare a highly skilled Blue card holder and a seasonal worker) is still viewed as the Other, a “subaltern” who needs ‘targeted’ accommodation. “Othering” in the present is done on the basis of the memories of cultural subordination in the past. On the other hand, assimilationism as represented in the French Republican model, shares the same rhetoric but offers different tools. It also propagates equality, understood as a basic commitment to human rights: according to the preamble to the French Constitution of October 27, 1946, which is integrated into the current constitutional corpus, “the French people proclaim again that every human being, without distinction of race, or religion or creed, possesses sacred and inalienable rights” (Preamble to the Constitution, 1946). An immigrant therefore is someone who is entitled to equal treatment simply due to the fact that he or she is a human being, not because he or she is a member of ethnic or racial minority group. Although immigration has never been a part of the “founding myth” of the French Republic, immigration and birthright citizenship (jus soli) have been linked to the evolution of Republicanism (Hollifield, 2010): from the early days of the Republic, politicians have appealed to Republican ideals of egalitarianism, universalism, and laicite as a way to legitimize immigration of ethnic minorities from former French colonies. In the 2002 presidential election, when French President Jacques Chirac competed with the right-wing anti-immigrant Presidential candidate Jean-Marie Le Pen, he appealed to all French voters to defend “the values of the Republic” and confront extremism. As James Hollifield (2010) puts it, “attacking immigrants and their rights is to some extent tantamount to attacking the Republic” (p. 157). 29 Paradoxically, the link between republicanism and immigration has created a threshold for the “Othering” of immigrants. Since immigration itself is framed as a manifestation of Republican values, a successful immigrant is expected to commit to these values: specifically, they must be able to separate church and state and keep their cultural difference and religious beliefs in the private realm. Those immigrants who are perceived as unwilling to do so, are labelled as “inassimilable” and “undesirable” immigrants. The idea of an ‘improper’, “inassimilable” immigrant is deeply rooted in the French colonial past. Similar to many empires, France declared that as an enlightened race, the French had a mission civilisatrice to educate the barbarians of the non-Western world. This “civilizing” and colonizing goal was largely determined by a distinctly secular Republican ideology. Whereas the British colonizers rejected the idea that “the subaltern native” may ever become British, the French believed that, if properly taught the French language and French Republican values such as secularism, the Algerian or Vietnamese could “evolve” into French citizens. This transformation is largely based on the idea of racial and cultural hierarchy where a non-white “subaltern native” is placed at the bottom but has the opportunity to rise to the top. This idea seems to remain as a foundation of immigrant integration policy in contemporary France. While multiculturalism and assimilationism offer opposite solutions to the challenges of immigrant integration, they both view immigrants as “the Other”. Under multiculturalism, the Other is encouraged to retain their cultural identity; under assimilationism, the Other is expected to voluntarily yield it. Otherness in both frameworks is understood as cultural distinctiveness which is often proxied as race and ethnicity; this understanding is inherently colonial. Why does this representation of immigrants matter? It matters because it largely 30 determines the direction of immigrant integration policy which in both cases targets immigrant, non-white or racialized white minorities. In multiculturalism, targeting means the individual’s entitlement to special policy treatment based on the status of the Other; in assimilationism, targeting means heightened expectations to adhere to majority rules which, again, are based on the status of the Other. In both frameworks, social class plays a secondary role to minority status since it hardly eliminates or reinforces ethnic-based targeting: in France, a highly educated, high-income immigrant does not achieve high status in the hierarchy of power (i.e. “becoming French”) unless they agree to conform to Republican values; in multicultural Britain, even a highly-educated high-income immigrant is eligible for group-differentiated targeted assistance by the state. Similarly, ethnic-based Othering eliminates the role of gender and other categories of social stratification. Un-Othering: Mainstreaming Instead of Targeting This dissertation views mainstreaming as an attempt to transcend the Othering of ‘ethnocentric’ integration frameworks. In this section, I will provide a definition of mainstreaming and explain, how “un-Othering” is related to policy mainstreaming in immigrant integration. The term ‘mainstreaming’ originated in the fields of gender and disability in education. Gender mainstreaming was accepted by the United Nations in 19971 as a strategy to incorporate a gender equality perspective in all policies, at all levels and at all stages. In the context of education, mainstreaming is the practice of placing students with special needs into general education classrooms during specific time periods. Broadly defined, mainstreaming involves bringing the contributions, needs and priorities of vulnerable or underrepresented groups to the centre of attention in more generic policies as opposed to 31 seeing specific concerns of these groups as separate policy areas. The purpose of the policy is to increase equality between vulnerable groups and majority populations. The area of immigrant integration is a more recent application of mainstreaming. The integration and citizenship literature distinguishes between three types of mainstreaming: ‘mainstreaming through discourse’ – a public narrative that incorporates immigrant integration into other goals;‘mainstreaming through governance’ – the practice of “coordinating a range of government actors on integration goals, either horizontally, by involving other policy departments at the same level, or vertically, by distributing responsibilities across multiple levels of government” (Colett & Petrovic, 2014; p. 2); and ‘mainstreaming through policy’, which is the main focus of my study. At the EU level, mainstreaming through policy was adopted in 2004, as a part of Common Basic Principles for Immigrant Integration Policy in the EU (2004). CBP 102 suggests that “mainstreaming integration policies and measures in all relevant policy portfolios and levels of government and public services is an important consideration in public-policy formation and implementation”. Mainstreaming was named a policy priority in the European Common Agenda on Integration (2005), the European Commission’s Integration Indicators (2011) and the Council of European Union’s Conclusions on Integration (2014). Based on the policy frames developed in the EU’s documents, the integration policy literature has produced several working definitions of integration policy mainstreaming. Elizabeth Colett and Milica Petrovic (2014) of the Migration Policy Institute Europe have defined mainstreaming as “reforms or adaptations of general policies that incorporate integration priorities” (p. 2). Petra Bendel (2014) has referred to “an effort to reach people with a migration background through social programming and policies that also target the 32 general population” (p. 4). Peter Scholten and Ilona van Breugel (2018) have explained that “mainstreaming refers to an amalgam of efforts to abandon target-group-specific policy measures and to coordinate integration measures as an integral part of generic policies in domains such as education, housing and employment” (p. 6)3. Based on the above, my study proposes the following definition of policy mainstreaming that combines the most crucial aspects of the new approach: a strategy intended to promote equality between migrants and nationals in generic policy domains through social programs that address the needs of the general population; targeting is problem-based rather than based on immigration status or ethnicity. What representation of the immigrant underpins this integration framework? In mainstreaming, an immigrant is viewed as an outsider who shares the same concerns and vulnerabilities as the general population. The focus is not on differences but on similarities between natives and newcomers. National models of integration create a separate entity for a group that is viewed as the Other due to sharing a set of common ethnocultural characteristics and address the perceived needs of this group through targeted integration policies. By contrast, mainstreaming downplays the ethnocultural characteristics by prioritizing the problems that are believed to be common for immigrants and other disadvantaged groups. Mainstreaming accounts for the fact that social class, gender identity, and disability might produce the same levels of vulnerability as immigrant minority status. All social groups who share a similar type of vulnerability are entitled to access the policies that help address it. In its policy documents, the European Commission claims that immigrant integration strategies “need not and should not be at the expense of measures to benefit other vulnerable or 3 Scholten and Breugel explain, that in some cases, “targeting by ethnicity or minority status is replaced by targeting by other, more generic conditions” such as problem-based steering (Scholten, 2018; p. 4). 33 disadvantaged groups or minorities” (Communities from the Commission to the European Parliament, 2016; p. 4). Like multiculturalism and assimilationism, mainstreaming has a strong focus on equality. While in assimilationism and multiculturalism, the idea of equality results in a policy response that implies non-discrimination or ‘special group-differentiated treatment’, in mainstreaming equality essentially means addressing vulnerabilities shared by minority and majority groups. While in multiculturalism and assimilationism an immigrant is considered to have a disadvantage simply due to their cultural characteristics, regardless of class, income and education, mainstreaming builds on the idea that where immigrants and majority population stand in the hierarchy of power depends on the human capital traits they possess. Cultural distinctiveness is placed outside this hierarchy. An immigrant is no longer “the subaltern native” but is rather considered to be equal to members of the majority population: there is an understanding that members of both groups can be equally disadvantaged or equally privileged. Not only does mainstreaming “bring class back in”, it breaks the link between race and class that had been inherently present in the long-standing national models of integration. Last but not least, it puts emphasis on other potential sources of social vulnerability such as gender, age, sexual orientation, physical or learning disability. It would be a mistake to say that mainstreaming completely ignores the ethnocultural dimension. Scholars distinguish between ‘indirect’ and ‘direct’ mainstreaming, the latter being focused on immigrant minorities as a target group. Most mainstreamed programs are indirect. According to Elizabeth Colett and Milica Petrovic (2014), “indirect approaches focus on addressing problems that may be shared by different types of vulnerable groups – immigrants and others” (p. 2). These initiatives tend to address areas of deprivation such as unemployment or social housing rather than offer a policy intervention based on a person’s 34 immigrant status, race or ethnicity. For example, under the indirect approach, immigrant women can benefit from the social programs that target all female participants regardless of residence status; similarly, unemployed third- country nationals can get assistance from employment support agencies that address the needs of all jobseekers. This approach is anchored in the principle of non-discrimination: both migrants and EU nationals have membership in the same target group with similar needs (e.g. women, the unemployed, low-income earners) and receive the same policy treatment. The indirect approach thus encompasses both access to policies and policy scope. Having immigrant status, which can by itself be a ground for discrimination, does not eliminate disadvantages based on other characteristics (class, gender, etc). Under the indirect approach to mainstreaming, immigrant status is an ever-present category in policy work but there is an understanding that other social and economic factors also play a role. By contrast, direct approaches target immigrants as a separate group within mainstreamed policies. These approaches address the specific needs of third-country nationals (e.g. providing language classes or labour market orientation activities) as a way to remediate inequality between them and EU nationals. The difference between direct approaches to mainstreaming and targeted group-specific immigrant integration strategies is that the former are embedded in the policies that address the general population (e.g. employment support) and the latter are a part of a stand-alone policy pattern that targets immigrants only (e.g. ‘civic integration’). For example, active information policy on the rights of migrant workers as a part of larger employment support policy is an example of a direct approach in mainstreaming. However, this ‘direct’ policy treatment is limited to the strategies aimed at better adaptation to the new country. It is not forced upon immigrants as a ‘ticket to acquiring 35 membership’ as in civic integration and in assimilationism; neither does it evolve into an expansive set of policies celebrating cultural diversity as in multiculturalism. ‘Targeted’ direct policies in mainstreaming are minimal and secondary to the rest of mainstreamed policies. My study focuses on the impact of both approaches to mainstreaming as intrinsic parts of the same policy strategy. Assessing which policy type is more conducive to immigrant integration is beyond the scope of my research. The policy shift from the targeted ethnocentric approach to mainstreaming is consistent with the recent phenomenon of “the diversification of diversity”– superdiversity (Vertovec, 2007). The term superdiversity or “a multi-dimensional perspective on diversity” (Vertovec, 2007, p. 1026) addresses the fact that the arrival of immigrants from different countries, combined with longer established minority groups, has resulted in an unprecedented variety of cultures, identities, languages, religions, labour market experiences, legal statuses and social classes. The “summary term” of superdiversity captures the interactions and conjunctions of these variables (Vertovec, 2007, pp. 1025-1026). There has been an understanding that superdiversity “emerged at a juncture where old concepts such as integration or assimilation as quasi-linear processes of immigrant incorporation had lost their explanatory power in terms of post-migration settlement and (moving-on) practices” (Meissner et al., 2015). Following Fran Meissner, I distinguish between superdiversity as a concept, a set of variables and a context. As a concept, it has important implications for understanding mainstreaming and its impact on labour market integration. Both mainstreaming and superdiversity emphasize the idea that cultural distinctiveness provides a misleading, one-dimensional appreciation of contemporary migration flows. Both frameworks agree that targeting increasingly heterogeneous immigrant 36 groups by using policies that address only one criterion of collective belonging, does not seem to be effective. It would be a mistake, however, to view policy mainstreaming in immigrant integration as a way to operationalize superdiversity. Mainstreaming obviously neglects the important variables of cultural distinctiveness, religion and race that capture a significant part of immigrant identity and might affect immigrant success in the labour market; superdiversity is much more inclusive as it encompasses multiple dimensions including race, religious and cultural differences. The concept of superdiversity helps highlight the controversial nature of mainstreaming as it emphasizes a mismatch between the limitations in policy scope and the actual variation in immigration cohorts. Policy Mainstreaming Why Mainstreaming? The reasons for the recent policy shift from targeted integration policy to mainstreaming are numerous; although my study is not intended to examine them in detail, I will name a few. First, European states have large numbers of second and third-generation immigrants. While the native-born European citizens of immigrant origin do not have to learn the host country language or ‘integrate’ in any other conventional way, many of them, especially those of lower socioeconomic status, face structural barriers to succeeding in the labour market (Colett & Petrovic, 2014). Targeted integration policies mostly address the needs of first-generation immigrants since the criteria for target group selection are based solely on immigration status; the needs of second and third-generation immigrants have been overlooked. Mainstreaming is viewed as a more comprehensive way to address this variation by including people of immigrant background into general policies. 37 One more possible reason for the European transition to mainstreaming can be growing public frustration caused by increased immigration to Europe in the last two decades and the limited ability of the state to address multiple challenges related to migration, including unemployment, social isolation of ethnic minorities from majority society, and negative public attitudes towards immigrants. Terror attacks in Europe in 2014-2016 fueled the anti-refugee and anti-immigrant backlash and reinforced widespread public perceptions that existing integration models such as multiculturalism have failed (Brubaker, 2001; Entzinger, 2003; Joppke, 2007). Mainstreaming has become a part of a wider process of reconsidering existing models of immigrant integration policy. Last but not least, the policy turn towards mainstreaming can be explained by the increased need for fiscal austerity (Colett & Petrovic, 2014). The economic downturn of 2008-2009 forced European governments to curb investing in targeted immigrant integration programs and prioritize policy strategies that are both cost-effective and capable to address the needs of the wider population – the trend that has the potential to develop further in the ‘survivalist’ economy of the age of coronavirus. Policy Areas in Mainstreaming: Labour Market Support As mentioned above, EU policy documents have advanced the view that mainstreaming should be applied in all policy areas. The Handbook on Immigrant Integration (2007), does not specify which policy areas are considered as key for mainstreaming, thereby implying that all are of equal importance. Examples provided in the handbook are drawn from the areas of education and employment, social affairs and public health. On the other hand, empirical studies of policy mainstreaming focus on specific national policies where mainstreaming has been more pronounced. Which policy areas have been prioritized by EU states depend on 38 multiple factors including but not limited to the existing national models of immigrant integration, which may indicate a certain level of path dependence4. Speaking of key mainstream policy areas in Denmark, Martin Bak Jorgensen (2014) underlines the importance of education, labour market integration policies and antidiscrimination. Sundas Ali and Ben Gidley (2014) argue that in the UK, mainstreaming integration has been effectively developed in three key areas: race relations and antidiscrimination legislation, social inclusion and social mobility, and community cohesion. In their analysis of German integration policies, researchers focus on employment and education. Policy preferences vary across EU states but most countries emphasize the importance of mainstreaming in employment and education. This study focuses on a policy domain that is central in immigrant integration: labour market support. Employment support combats some major issues faced by third-country nationals on their way to permanent employment, such as the lack of information about job market in the host country, lack of work experience in the host country, lack of culture-specific ‘soft skills’, low language proficiency. In addition to these immigrant-specific issues, newcomers share the same burdens as low-income, disabled or otherwise disadvantaged EU nationals. Mainstreaming in employment support is aimed to mitigate these obstacles by providing counselling services and access to vocational training and apprenticeships that often lead to employment opportunities; facilitating contacts between immigrants and nationals in a working environment and thereby eliminating the social and cultural isolation of ethnic communities and creating more opportunities for networking and language practice. 4 For example, mainstreaming in France involves a strong area-based component: it involves work in select poor disadvantaged neighbourhoods that have a high concentration of immigrants. As Angeline Escarfe-Dublet (2014) explains, the area-based approach to integration takes its roots from the Republican-assimilationist idea of equality regardless of race and religion. The neighbourhoods are therefore targeted due to the fact that they are poor. 39 National and Local Variation in Policies A few words should be said about national and local variation in mainstreamed policies. First, in a number of EU countries, immigrant-related issues are associated with particular localities, such as banlieues, France’s troubled suburbs, where more than half the residents are of foreign origin and unemployment is over twice the national level. Since foreign populations tends to be more concentrated in certain regions (e.g. urban areas), some localities may have better developed immigrant integration strategies including policy mainstreaming than others. Further, while in some EU countries, such as the UK, the system of integration governance has multiple tiers (each with a large degree of policy discretion), in countries such as Denmark and Sweden, policy efforts in the area of integration are more centralized. Depending on the patterns of immigrant concentration and the degree of institutional pluralism, mainstreamed policies may vary at both the local (city, provincial, suburban) and the national level. The scope of my study is restricted to the national level of mainstreaming. The decision to exclude the local dimension of immigrant integration policies is determined by the fact that local policies, as some of recent studies show (Scholten 2013), are often uniquely shaped by their local political and social context and thus are not suited for cross-country comparison as there are too many factors that should be controlled for. These differences only allow for within-country comparison which cannot be extended to other EU states. The Outcome Variable: Labour Market Integration The outcome variable, labour market integration, needs clarification. Although there is no strong consensus on a single definition of immigrant integration in the EU, there is a broad understanding that it is a part of “the process by which immigrants become accepted 40 into society, both as individuals and groups” (IOM, 2011, p. 51). Integration frameworks have three major dimensions: socio-economic integration, which involves equality with respect to “access to the labour market, earnings, education and training, housing, benefits, and social services” (Lodovici, 2010, p. 2); political integration, i.e. maintenance of citizenship rights, and cultural or socio-cultural integration, i.e. adherence to the host country’s norms and values. Labour market integration is a major component of socio-economic integration. Since labour market integration incorporates the concept of equality, it signals the convergence between immigrant and native populations with respect to employment status. In simple terms, successful labour market integration indicates the elimination of employment gap between EU nationals and immigrants. Besides, labour market integration can be understood in absolute terms: as obtaining employment that leads to financial self-sufficiency and eliminates dependency on welfare. Such employment is the goal pursued by both immigrants and EU-nationals; however, immigrants face greater barriers and hurdles in the labour market. The following section sheds light on some demographic attributes of immigrants that might be associated with such barriers. Immigrants in the Sample: Who Are They? Who exactly are the immigrants included in the study? What characteristics do they possess? While the main inference of interest in this study is the labour market integration of third-country nationals, I acknowledge more nuanced variation by distinguishing between third-country nationals (TCNs), the EU nationals who live in their own country, and internal migrants from other EU countries (intra-EU migrants). This distinction is important because it accounts for the fact that some groups of intra-EU migrants, especially Polish and other 41 Eastern European workers, face greater challenges than resident nationals in the Western European labour market. The challenges relate to both discrimination and the lack of essential job skills such as poor knowledge of the host country’s language. In what follows, I will provide some characteristics of the EU residents across the three groups of EU residents. The LFS sample is limited to 26, 712 TCNs, 24, 647 intra-EU migrants and 652, 573 EU nationals living in their own country (see Table 1). As shown in Figure 1, third-country nationals come from all parts of the world, the most popular origin places being European countries outside the EU, e.g. Georgia, Ukraine, Russia (35%); Africa (28%); and South and South-East Asia (13%). Immigrants from North America and Australia represent the smallest group of TCNs in the sample (2.86% cumulatively). At the average age of 40, third-country nationals are nine years younger (M = 40.10, SD = 0.08) than the EU nationals living in their native country (M = 49.26, SD = 0.02), t = 98, p = 0.00. The average age of intra-EU migrants in the sample is 42 (M = 42.46, SD = 0.09), t = 68, p = 0.00. The percentage of men and women does not differ across the three groups: as shown in Table A1, in each category of EU residents, there are 47-48% of men and 52-53% of women. Importantly, among third-country nationals, the number of individuals with low level of education is much bigger than among the EU nationals (see Table 2). While only 26.61% of intra-EU migrants and 36.21% of host-country nationals are low-educated workers, almost half of TCNs fall into this category. Similarly, the share of workers with medium and high levels of education among TCNs is smaller than among EU nationals: 31.29% and 21.72%, respectively. Among intra-EU migrants, the level of highly-educated individuals is the highest across the three groups (34.94%). 42 Table 1 The Distribution of EU Residents (LFS-2018) Three categories of EU residents Frequency Percent Host-country nationals 652, 573 92.70 Intra-EU migrants 24, 647 3.50 Third-country nationals 26, 712 3.79 Total 703, 932 100.00 Figure 1 The Distribution of Third-Country Nationals by the Place of Origin (LFS-2018) Note: The bar chart does not represent countries whose data form part of the anonymized LFS data. 43 Table 2 Levels of Education by Categories of EU Residents (LFS-2018) Levels of education Three categories of EU residents Host-country nationals Intra-EU migrants Third-country nationals Low 36.21% 26.61% 47.00% Medium 37.39% 38.45% 31.29% High 26.40% 34.94% 21.72% Note: According to the International Standard Classification of Education used in the LFS, the low education level includes primary and lower secondary education. The medium education level includes upper secondary education and vocational training. High level of education refers to post-secondary education including undergraduate and graduate credentials. Due to data limitations, there is no access to other immigrant attributes in the LFS-2018 dataset. However, although the information presented above is limited, it highlights some important patterns. First, more than half of TCNs in the sample are from non-white majority regions. This points at the likelihood of racial discrimination and has important implications for why TCNs might have faced challenges during job search in a new country and look for a new strategy. Second, the number of North American immigrants is negligibly small which indicates that the sample is dominated by third-country nationals from rich privileged countries. Third, 47% of TCNs are low-skilled immigrants, which may contribute to the immigrants’ decision to increase their human capital by acquiring new professional skills through labour market support programs. The Effects of Mainstreaming on the Labour Market Integration of Immigrants Having identified the key variables, policy mainstreaming and labour market integration, I proceed to their interaction, i.e. the mechanism of policy impact. This section 44 theorizes the link between policy mainstreaming and the labour market integration of immigrants in the EU. The proposed relationship works as follows: higher levels of mainstreaming in labour market support are positively correlated with immigrants’ policy exposure; the increase in exposure is conducive to better employment outcomes for immigrants. The policy impact refers to the level of equality in employment outcomes between third-country nationals and EU nationals established as a result of involvement in mainstreamed policies and programs. As discussed earlier in the chapter, equality is a foundational concept in mainstreaming. To reiterate, mainstreaming considers immigrants to be ‘equal’ to majority group members in that both groups share the same concerns and vulnerabilities that can, among other reasons, stem from a lack of skills, education, work experience or disability. Mainstreaming reinforces the idea that immigrant minority groups and majority populations can be equally disadvantaged due to the characteristics that transcend race and culture. Unlike alternative integration frameworks, mainstreaming is directed at immigrants and non-immigrants alike. Since immigrants and non-immigrants are viewed as equal, mainstreamed policies treat them as members of the same target group. The question is whether such an ‘equal’ policy treatment approach, with relatively little focus on ethno-cultural characteristics, can lead to equality in labour market outcomes. Equality in mainstreaming is both a goal and a modus operandi: greater equality in policy exposure is supposed to result in greater equality in the labour market – in other words, in the reduction of unemployment gap between the two groups. If, as a result, third-country nationals lag behind their EU counterparts in employment growth, this could be an indication that the focus on socio-economic attributes is not feasible and that some other factors, such as immigrant-specific problems, could hinder 45 the labour market performance of third-country nationals and widen the gap between the two groups. Measuring the level of equality in employment outcomes is one way to test the effectiveness of mainstreaming as an integration approach. I measure the impact of mainstreaming both in absolute terms, i.e. by examining its overall effect on employment outcomes, and in relative terms, by looking at which group of EU residents, third-country nationals or EU nationals, benefits more from employment assistance. To make inferences about equality, we need to make intergroup comparisons. EU nationals serve as a reference category against which we compare the outcomes for TCNs. If mainstreaming works, we expect to see outcomes that at the very least do not fall behind the results demonstrated by EU nationals. The argument consists of two components: the level of policy exposure for EU nationals and TCNs, and the relationship between policy exposure and labour market outcomes for both groups. If mainstreaming works, we should, first, expect to observe increased equality in policy exposure under higher levels of mainstreaming for immigrants and non-immigrants. Second, under higher levels of mainstreaming, we should expect to observe a robust relationship between policy exposure and labour market outcomes under which immigrants keep up with their European counterparts. The first component, policy exposure, marks the point at which jobseekers get in contact with PES and receive policy treatment. Policy exposure can involve contacting PES and placing their name into a database to be contacted later. This action does not indicate any particular commitment on part of the jobseeker. However, policy exposure is not limited to occasional phone calls to the local PES. It can entail immigrants’ formal enrollment in labour market support programs, i.e. registration with PES which suggests consistent attendance and effort. Many labour market support programs are only available to those jobseekers who are 46 officially registered at national public employment services. It is therefore feasible to make claims about policy effects, based on the link between registration and labour market outcomes. Now we have a question of why we should expect to observe higher levels of equality in policy exposure and in labour market outcomes between the two groups, immigrants and non-immigrants, under higher levels of mainstreaming. ‘Mainstreaming’, as measured in the Index of Mainstreaming in Labour Market Support, represents a combination of scores in two categories, ‘policy scope’ (set of labour market support policies and programs implemented by PES) and ‘policy access’ (levels of eligibility for various groups of immigrants). Chapter 4 elaborates more on the system of measurement and the structure of the Index. The link between higher ‘access’ and higher exposure is quite intuitive. We should expect more equality in exposure under higher levels of mainstreaming because the more categories of immigrants have access to employment support programs, the more immigrants participate in them. More restrictions in access result in fewer immigrant jobseekers at PES. The relationship between ‘scope’ and policy exposure is more nuanced. ‘Scope’ is measured as the amount of government spending on each labour market support policy. I argue that more generous state funding is likely to lead to an increase in the expansion and development of employment support programs. It is likely to make policies more appealing for jobseekers which, in turn, leads to higher policy exposure. Indeed, immigrants’ consent to participate in labour market support programs by registering with PES implies their familiarity with the scope and the structure of the programs. Immigrants are more likely to sign a contract with a PES that has more to offer. The relationship between policy exposure and labour market outcomes develops as follows. Both dimensions of mainstreaming contribute to the process. Through indirect 47 mainstreaming, labour market support policies address employment-related concerns shared by immigrants and other vulnerable groups. For example, both immigrants and non-immigrants benefit from apprenticeship programs for low-skilled jobseekers who lack work experience and/or vocational training. Indirect mainstreaming is also manifested in ensuring equal access to labour market supports for third-country nationals. In addition to indirect policies, immigrants receive ‘special treatment’ through direct mainstreaming targeted at individuals with group-specific concerns. Examples of such policies include programs designed to enhance migrants’ language skills; or to provide a job market orientation in a host country. This set of policies serves as a remedy for the effects of race-based discrimination that immigrants are disproportionally exposed to in the EU. ‘Direct’ policies comprise a minimal portion of mainstreamed policies as compared to ‘indirect’ treatment. Due to its intention to embrace multiple ‘shared’ aspects of social vulnerability, policy mainstreaming in labour market support has an ambition to enhance the labour market integration of third-country nationals both in relative and absolute terms: it increases the convergence between immigrants and native populations with respect to employment status, and, on average, increases the probability of obtaining permanent employment and financial independence for immigrants. This study examines whether this ambition leads to policy success. 48 Figure 2. The Mechanism of the Relationship Between Mainstreaming and Labour Market Outcomes Based on the above, this study proposes the following hypotheses. H1: Third-country nationals in the EU have the same or higher level of policy exposure to labour market support as EU nationals. H2: Third-country nationals in the EU find employment through PES as often as or more often than EU nationals. H3: Higher policy exposure of TCNs to mainstreamed labour market support is correlated with better job market performance (for TCNs). H1 – H3 test individual-level relationships: between immigrant status and policy exposure, immigrant status and labour market outcomes, and between policy exposure and labour market outcomes. H1 and H2 look at the level of equality (which is the key idea of mainstreaming) between immigrants and non-immigrants by making claims about the average levels of policy exposure (H1) and policy effects (H2) among immigrants as compared to EU nationals across the EU. H-3 makes a statement on the average effect of policy exposure on labour market outcomes. If H3 is confirmed, mainstreaming as a labour market integration strategy can be considered effective in absolute terms, i.e. not in comparison with EU nationals. H1 – H3 test the average effect across the EU, without accounting for country-Increased levels of mainstreaming: more access and increased spending on scopeIncreased exposure Better labour market outcomes for immigrants 49 specific variation in mainstreamed policies. Does mainstreaming matter or is it due to other factors, e.g. the characteristics of PES? In other words, does the level of mainstreaming have any impact on the relationships specified in H 1 – H3? To address this question, I propose the second set of hypotheses. Each of them tests a country-level effect: it suggests that individual-level relationships specified in H1 – H3 are more robust in EU countries with higher level of mainstreaming in labour market support. H4: The effect of immigrant status on policy exposure is stronger in countries that score higher in mainstreaming in labour market support. H5: The effect of finding employment through PES is stronger for third-country nationals than for EU nationals in countries that score higher in mainstreaming in labour market support. H6: The effect of policy exposure on TCNs’ labour market outcomes is stronger in countries that score higher in mainstreaming in labour market support. The hypotheses are intended to test the overall claim that mainstreaming – a framework that offers an alternative appreciation of diversity – correlates with better labour market performance of TCNs. If the claim is true, it will contribute to our knowledge of whether transcending the ethnonational attributes of immigrants and focusing on vulnerabilities shared by multiple groups is an effective policy strategy. 50 Chapter 3. Research Design, Index Building and Policy Impact Assessment In the area of citizenship and immigrant integration, there has been a noticeable increase in the number of policy indices that quantify and rank regulatory measures at the national level (Helbling & Vink, 2013). Yet there has been no attempt to operationalize mainstreaming by building a policy index that would allow us to draw cross-country comparisons and examine policy patterns in more detail. This study aims to fill this gap by proposing the Index of Mainstreaming in Labour Market Support (the MLMS). Index-building and policy impact assessment have been challenging methodological procedures because they raise the following questions: How do we measure and compare integration policies? Do the indices measure what they intend to measure? Do individual index scores consistently measure the composite score they are a part of? How do we estimate the effectiveness of policy outputs? This chapter tackles these questions by discussing the background and rationale for the research design used in this study. This study uses a two-stage methodological approach. First, I designed and compiled the MLMS Index as a strategy of empirically capturing variation in policy mainstreaming in labour market support across the EU/EEA. Second, I combined the policy data obtained from the index with individual data drawn from the EU Labour Force Survey (EU LFS) to test the second part of the theory – the policy effect on the economic integration of immigrants in the EU. I conducted a single-level logistic regression analysis, cross-tabulation analysis and used Bayesian multilevel regression to measure the effect of migrant exposure to employment support and the effect of policy mainstreaming on immigrant integration in the labour market across the EU. My 51 methods of analysis are mostly quantitative but I also use a case study analysis to contextualize mainstreaming and to identify immigrant-specific targeted measures in employment support. Following this two-stage methodological strategy, this chapter consists of two sections: one is devoted to index-building, another to policy impact assessment. In the first section, I explain, how I gathered and selected material for the MLMS Index. Next, I focus on two crucial aspects of index-building, validity and reliability. I address the problem of content validity by focusing on two dimensions of the MLMS Index - ‘access’ and ‘scope’- and address the strengths and limitations of my measurement strategies. Finally, I measure the reliability (understood as internal item consistency) of the composite score in the MLMS Index by using Cronbach’s alpha. In the second part, I explain, why this study uses different types of regression analysis and the Bayesian framework in particular as a method of policy impact assessment. The final section of the chapter explains my selection criteria for case study analysis in Chapter 5. Index-Building Strategy The MLMS Index: the Overview and Data Collection The MLMS Index examines and categorizes mainstreaming in labour market support across the EU/EEA. The index has two dimensions: it measures the scope of policy mainstreaming in labour market support and the access to mainstreamed policies. As mentioned in Chapter 2, the scope dimension covers a variety of labour market support policies and programs implemented by Public Employment Services. ‘Access’ refers to the levels of eligibility to these policies and programs for various groups of immigrants (as shown in Table 1). For ‘scope’, the statistical unit of data collection is public spending per one policy category (e.g. “Training’, ‘Direct job creation’), measured as a percentage of GDP. For ‘access’, the statistical 52 unit is a national regulation that stipulates which categories of immigrants have access to labour market support (can be registered with Public Employment Services that are responsible for providing this support). There are two versions of the MLMS Index. One is an extended dataset that covers the data from 2006 to 2016 across 25 EU/EEA countries5. I also created a dataset that only covers the most recent available data from 2016; this version, matched with the most recent available individual-level data from the EU-LFS, is used for policy impact assessment. Chapter 4 presents the MLMS Index in more detail by focusing on both policy dimensions, policy variation within each dimension, coding rules and policy patterns across Europe. This section focuses solely on data collection. The Index is based on the policy data from three sources: the Eurostat Labour Market Policy (LMP) database provided by the European Commission’s Directorate General for Employment, Social Affairs and Inclusion (both quantitative data and qualitative policy reports); the Migrant Integration Policy Index (MIPEX); and administrative sources in each EU country. The data for the ‘scope’ dimension have been drawn from the LMP database. The data for the ‘access’ dimension have been drawn from the MIPEX and various administrative sources such as national PES regulations and other data available in the public domain (e.g. the websites of national public employment services). I used the LMP database as a data source for the MLMS Index because it provides the most comprehensive available information about labour market interventions used by European states to help the unemployed and inactive jobseekers in their transition from unemployment or 5 Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, the United Kingdom. 53 inactivity6 to work. The LMP database is used for monitoring the EU Employment Guidelines through the Europe 2020 Joint Assessment Framework (JAF) – an indicator-based assessment system developed and used by the European Commission, the Employment Committee and the Social Protection Committee (Labour Market Policy Statistics 2018). The LMP data contains information about (1) public expenditure on employment support and (2) participants. Since the scope dimension in the MLMS dimension is measured by calculating the total amount of public spending on the ‘active’ labour market support policies, I only used the information about expenditure – specifically, three data sources: summary tables of expenditure by type of action; detailed tables of expenditure and participants by intervention; and qualitative reports that describe policy interventions in each country. The qualitative reports were mostly used to estimate variation in employment support policies targeted at third-country nationals (‘direct mainstreaming’) and to obtain more general policy information on Denmark, France and Slovakia for case study analysis. I used policy descriptions in qualitative reports to make inferences about a dominant policy orientation during a specific year (e.g. Slovakia 2016). For example, if most policies fell within a category of ‘job training’, one can infer that the dominant pattern was supply-side measures under which governments ‘activate’ jobseekers by providing them with a set of skills. In the MLMS Index, I used the same classification system as in the LMP data, for the sake of consistency with policy definitions accepted across the EU/EEA: grouping the interventions into ‘measures’, ‘services’ and ‘supports’ and using the same definitions for policy subcategories within each group (e.g. ‘training’, ‘direct job creation’) as shown in Table 1. 6 Inactive jobseekers are individuals who are not in paid work and are not looking for a job or are not available to start work. 54 The LMP data, however, does not contain information about third-country nationals as a separate target group or about their access to employment support: the data on participants covers their gender, age and unemployment duration, not their immigration status. To create the ‘access’ policy dimension in the MLMS Index, I used disaggregated data from the MIPEX. The MIPEX covers 38 countries including all EU/EEA member states, and has 197 policy indicators in 8 policy strands: ‘labour market mobility’, ‘family reunion for foreign citizens’, ‘education’, ‘political participation’, ‘permanent residence’ ‘access to nationality’, ‘antidiscrimination’, and ‘health’7. I used the data from the ‘labour market mobility’ policy strand: one of its policy dimensions (subcategories), ‘access to general support’, contains information about the levels of access to public employment services for three categories of third-country nationals: permanent residents, work permit holders, and residents on family reunion permits. This information has been cross-validated, updated and complemented by data on national regulations on registration at PES for third-country nationals. The data include the information on policy access available in the public domain (websites of Public Employment Services) and in national legal documents. 7 I agree with the scholars who see the number of indicators in the MIPEX as redundant and the MIPEX itself as lacking strong theoretical foundation; however, as Ruedin (2012, 2015) tells us, the large number of indicators is a strength rather than a weakness in that it makes it possible to regroup and recombine individual policy items in accordance with specifications other than those used in MIPEX publications. 55 Table 3 The MLMS Index: Dimensions and Categories Dimensions Categories Scope Vocational training/apprenticeship Employment incentives Direct job creation Start-up incentives Counselling/advisory services (language market orientation, skills assessment) Language training Access Only EU nationals Permanent residents Residents on temporary work permits Residents on temporary family reunion permits Refugees (with recognized status) Types of Validity. Index-building is a methodological procedure that is based on establishing the validity and reliability of measurement. As the number of indices in the area of citizenship and integration started to increase in the second half of the 2000s – “there are almost as many indices as there are studies” (Helbling, 2013, p. 555), the literature on indices started to address methodological concerns relevant to index-building – specifically, validity questions. In its most general sense, the validity of measurement indicates “how well indicators of the phenomenon under consideration actually represent the ideas to be measured and whether the indicants are recorded with precision” (Salkind, 2010, p. 1593). In the literature, validity has been discussed in the context of index comparison, as an effort to embrace the growing number of policy indices and to address their commonalities and differences, weaknesses and strengths 56 (Helbling, 2013; Goodman, 2019). The literature on policy indices draws a distinction between content validity, construct validity and convergent validity (Bollen, 1989; Helbling, 2013). Content validity is based on the idea that the index measures a certain content domain, and that all the items (policy indicators) in the index are representative of this domain. The discussion and measurement of content validity involve careful conceptualization of policy fields. Convergent validity entails “the comparison of alternative measures of the same concepts” and is measured with correlation tests that evaluate whether two or more indices are similar, i.e. whether they can converge (Helbling, 2013, p. 562). Finally, construct validity discusses whether there is a consistency among inferences or predictions derived from different variables that capture the same concept across different citizenship indices; construct validity can be tested by applying the same explanatory model to the different indices. Both convergent and construct validity are mostly used in index comparison, which is beyond the scope of my study. Besides, little effort has been made to build indices to measure policy mainstreaming, so there is hardly any ground for comparative analysis. I will therefore focus on content validity. My key assumption with regard to content validity is that it can be evaluated at two levels: as the capacity of the index to represent the content domain, i.e. ‘immigration’ or ‘integration’; and as the extent to which the scores from a policy indicator represent the content of the subconcept/indicator (in my case – policy access and policy scope). In what follows, I will address both aspects of content validity. 57 Figure 3 Content Validity Across Two Levels Validity of the Content Domain. I will start with explaining, what ‘content domain’ entails. The citizenship and integration literature differentiates between indices that focus on policy outputs and those that focus on policy outcomes; sometimes these two aspects are combined (Helbling, 2013, p. 558). Policy output indices include “legal rules and regulatory measures aimed at achieving social outcomes” (Helbling & Vink, 2013, pp. 552). “Policy outcomes” are the consequences of these policies. There is a differentiation within each group (i.e. ‘policy output index’ and ‘policy outcome index’) between two ‘content domains’, immigration and integration. Immigration involves “policies that determine who can enter or exit a sovereign territory”, while integration policies focus on “member shaping, namely successful incorporation into the host society in terms of acquiring skills (language, values) and behavior across any number of dimensions, including social, cultural, civic, economic, and political” (Goodman, 2019, pp. 574-575). In accordance with this classification, the MLMS Index covers policy outputs in the area of immigrant integration by focusing on the access to employment support policies for immigrants and the empirical scope of these policies. More specifically, the MLMS Index covers a particular type of integration – policy mainstreaming in labour market support. Level 2: Validity of the content domainLevel 1: Validity of policy indicatorsLevel 2: Integration domainLevel 1: Policy scope and policy access 58 One of major methodological concerns in index-building is concept differentiation or, as Sara Wallace Goodman (2019) puts it, “the issue of conceptual boundary maintenance” (p. 583). Although Goodman does not discuss this issue in the context of content validity, I argue that her argument can be applied to content validity as well. Whether a policy index is representative of a policy domain is conditional on how clearly the boundaries of the domain have been defined. According to Goodman, many integration indices including the Migrant Integration Policy Index (Migration Policy Group 2011), the Multiculturalism Policy Index (Banting & Kymlicka, 2013), the Indicators of Citizenship Rights for Immigrants (Koopmans & Michalowski, 2017; Koopmans et al., 2012) include aspects of both immigration policy and integration policy. For example, the ICRI combines policy indicators from both subfields, e.g. nationality acquisition and family reunification (immigration domains) and cultural rights in education (integration domain)”. This implies that “everything is integration” (Goodman 2019, p. 583), which makes boundary maintenance complicated. Goodman (2019) proposes a priori categorization and specification as a way to resolve this problem. Following her approach, I will address my choice of measurement strategies and explain their relevance to the purpose of the study. In the MLMS Index, the ‘access’ dimension is an immigration policy indicator that is operationalized as a type of immigration status. The ‘scope’ dimension, on the other hand, covers integration policies in the area of labour market support that target both immigrants and non-immigrants. Two questions arise here: first, whether it is relevant to include an immigration policy indicator (‘access’) in the ‘integration’ index, and secondly, whether the integration dimension (‘scope’) is problematic because it covers policies that address both immigrants and non-immigrants. 59 As I have argued above, the ‘content domain’ of the MLMS Index is policy mainstreaming, conceptualized as a integration strategy in the EU. As discussed in Chapter 2, mainstreaming is a policy effort aimed at establishing equality between EU nationals and immigrants from outside the EU with regard to policy exposure and policy effect. This focus on equality stipulates measurement strategies in both dimensions of the MLMS Index: measuring ‘scope’ in reference to both immigrants and non-immigrants, and using an immigration policy (‘access’) in the integration index. Equal policy exposure, which is a necessary condition for receiving policy treatment, is conditional on whether third-country nationals have the same level of access to employment support policies as EU nationals, i.e. are able to register with PES. The ability to register is, in turn, determined by their immigration status. The ‘access’ policy indicator is therefore key in measuring mainstreaming. By the same token, the ‘scope’ dimension covers policies that address both immigrants and non-immigrants because equality, i.e. incorporating immigrants into a wider target group that includes all EU residents, is the purpose and essence of mainstreaming. In other words, ‘integration’ is understood as accommodating the needs of immigrants by not addressing them as a separate target group. This distinguishes the MLMS Index from other integration indices that cover only immigrant-oriented programs. Both measurement strategies are therefore meaningfully related to the purpose of the study. Validity of Policy Indicators. Content validity can also be estimated at a lower level, by looking at whether a measure captures the meaning of the policy indicator. In measuring ‘access’ to labour market support, I follow ‘conventional’ measurement practices: as most other immigration and integration indices that focus on policy outputs, the MLMS Index relies on national-level legal regulations. ‘Access’ is not a multidimensional composite construct; it can be 60 measured by an individual item score, i.e. a single policy that stipulates what categories of immigrants can register with national public employment agencies. ‘Scope’, on the other hand, is a composite item that includes multiple policy categories (e.g. ‘employment incentives’, ‘training’, ‘direct job creation’ etc.) and multiple policy items within each category. The question is whether scope can be measured simply by the count of policy items falling under each category. The qualitative reports drawn from the Eurostat Labour Market Policy database show that across the EU, policy items within each policy category vary in terms of coverage, scope and duration. What is considered as a single policy item in the Eurostat qualitative report can, in fact, include one or several policy interventions (e.g. apprenticeship; apprenticeship and counselling services; apprenticeship in a specific industry); address one target group or multiple groups (e.g. only the Roma minority; all registered jobseekers); have a limited duration (e.g. several months, one year, five years) or be identified as ongoing. In other words, there is a boundary maintenance issue at the policy level: in labour market support, the boundaries of a unit of measurement (i.e. ‘policy’) remain fuzzy due to significant country-level variation in measurement standards used by Public Employment Services across the EU. Counting the number of policies does not therefore seem to be a productive approach. Instead, the MLMS Index measures policy variation as a percentage of GDP spent on each policy category. This strategy has its strengths and limitations. Its biggest strength is that it sets a unified system of measurement across the EU that allows us to make comparisons across countries. Furthermore, since the data are available for each policy category, cross-country comparisons can be more nuanced. The limitation of this system of measurement is that it does not account for the substantive variation within each policy 61 category – for example, the programs that are targeted at immigrants as a separate group. In other words, it does not account for the policy variation within the category of ‘direct mainstreaming’. There is very limited data that capture how much funding (measured as percentage of GDP) governments spend on immigrant-specific programs within the PES framework. Counting the number of policies is of little help here as well because the same issue – variation in duration, scope and coverage within each policy category – prevents us from relying on the comparative analysis of policy content. The solution proposed in this study is to rely on case study analysis which facilitates our understanding of policy scope. Reliability. Reliability is an important aspect of index-building that is often overlooked in the literature on integration and citizenship indices. While there has been an extensive discussion of various types of reliability in the context of scale-building (surveys), especially in the psychometric literature, in political science reliability is commonly understood and explained mostly as an external attribute of measurement. It tells us whether the same result would be found if the experiment was replicated by different scholars. There has been little discussion of internal reliability or internal consistency, which is solely based on the attributes of the index. Since indices and scales have significant similarities (most importantly, both are designed to measure composite constructs with multi-dimensional attributes and both use multiple items to capture these attributes), it is important to discuss alternative (internal) aspects of reliability that are relevant to index-building. In its most general sense, the concept of reliability is premised on the idea of consistency: that a measurement produces the same result if the test is applied to the same group of respondents at a different point in time (test-retest reliability), if different researchers assess the same variable using different samples (interrater reliability), if different tests or indices measure 62 the same concept (parallel forms reliability), and if a set of multiple items measure the same concept (internal consistency). Since neither index comparison nor replication are the goals of my study, I exclude interrater reliability and parallel forms reliability from the analysis. Test-retest reliability is typically used when a survey is administered on multiple occasions on the same subjects and under similar conditions; this type of reliability is used as a tool to estimate measurement error and to compare the accuracy of responses. In principle, this type of reliability could be applied to the Labour Force Survey to be estimated with panel data analysis. However, as indicated in Chapter 4, the structure of the Labour Force Survey does not allow for a comparison of the measurement of particular variables over time; besides, measuring the reliability of the LFS is beyond the scope of my research8. In what follows, I will therefore focus on the internal consistency of policy items in the MLMS Index. Internal consistency in my case estimates the extent to which the items in the MLMS index correlate with each other. Internal consistency is only relevant to composite scores, i.e. the scores that represent the average or the sum of two or more scores. The MLMS Index сontains one multidimensional concept represented as a composite score: ‘policy scope in mainstreaming’ that consists of five policy categories or dimensions of mainstreaming (‘training’, employment incentives’, ‘direct job creation’, ‘supported employment and rehabilitation’, ‘start-up incentives’). To estimate the extent to which these items are correlated, I use Cronbach’s alpha, the coefficient of internal consistency. It is defined as: α = ! с$% !&' ( 8 Although most countries have a rotation scheme in place, the member states decided not to allow the tracking of persons across waves for the purpose of data protection; the household numbers are randomized per dataset. 63 where k refers to the number of items in the index, c refers to the average of all covariances between items and v refers to the average variance of each item. Cronbach’s alpha ranges from 0 to 1, where 0 means weak association among the individual items and α = 1 indicates that all items positively and strongly co-variate with each other. The higher the α coefficient, the more correlation among items we observe, which indicates that they are likely to measure the same common underlying construct. The measurement has been applied to the full version of the MLMS Index that covers the 2006 – 2016 time period. Table 4 Testing Internal Consistency in Policy Scope Average inter-item covariance 15,736.95 Number of items: 5 Reliability coefficient: 0.98 Table 2 shows that there is a high level of internal consistency/reliability among the five policy items that measure policy scope in labour market support. Cronbach’s alpha is 0.98, which means that 98% of the variance in the composite score of policy scope is reliable variance. All the items that measure policy scope are highly correlated and are highly likely to measure the same policy item. Case Study Analysis: Criteria for Case Selection The purpose of the qualitative component in Chapter 5 is to provide more empirical context for the MLMS Index and establish correlations between mainstreaming and other policy 64 frameworks. The cases are not representative of the entire policy variety in the Index but instead are chosen to substantiate the Index scores with detailed descriptions of what variation looks like on the ground. Each case is supplemented with a table that provides a list of mainstreamed policies with descriptions that elucidate the features of each policy. I use a diverse-case strategy of case selection (Seawright & Gerring, 2008): to address the variety of mainstreaming regimes, I have chosen cases lying in the middle and at the both tails of distribution, i.e. the cases that have high, medium, and low scores in both dimensions of the MLMS Index. Denmark scores highest in policy ‘scope’ and has a medium-high score in ‘access’. France has an average score for ‘scope’ and the maximum score for ‘access’. Finally, Slovakia is a low-scoring case for both dimensions, which is typical of Eastern European states. In the case studies, I examine the country-specific context of mainstreaming. The case studies discuss the labour market structure in each country as well as immigrant demography, in particular the gaps between immigrant and non-immigrant employment rates. Policy-wise, I focus on employment protection and social security (welfare policy). In most European countries (including the three countries in question), unemployment protection regulation and social security policy had already been in place before mainstreaming was implemented which is why it is relevant to examine how the functional performance of mainstreaming was affected by these frameworks. I also analyze the political context in which integration policy is set. In particular, I examine the political framing of mainstreaming in the three European states, including the role of partisanship in politicizing the issue of integration policy, and labour market integration in particular. 65 Policy Impact Assessment In political science, the efforts to quantitatively examine the effects of a policy on an outcome of interest follow either of two directions: 'conventional' regression analysis or quasi-experimental designs, i.e. counterfactual methods such as regression-discontinuity analysis or difference-in-difference methods. In what follows, I will explain why I chose regression analysis, and multilevel Bayesian modelling in particular, as the methodological strategy to measure policy effect, and what strengths and limitations this approach has. I will discuss my method of cross-validation and explain, why I use fixed-effects single-level regression in combination with multilevel regression. Regression Analysis as a Strategy to Measure Policy Impact Both regression analysis and quasi-experimental designs have been used to estimate the policy causal effect, that is, the extent to which variation in the outcome can be attributed to the policy intervention, holding all other things constant. However, different methods address distinct research interests. Quasi-experimental designs are used to estimate the average treatment effect of a policy intervention on a group of participants (‘intervention group’ or ‘treatment group’), the reference point being the group of non-participants (‘control group’), e.g. the immigrants who did not receive the policy treatment. ‘Conventional’ regression analysis is mostly employed to either evaluate the group-level effect of policies on integration outcomes or to address the difference in policy effect between groups, e.g. between immigrants and non-immigrants. The group-level effect is evaluated by looking at whether there has been any meaningful variation in policy impact across groups (i.e. countries). For example, we can examine the question whether immigrants in countries with more advanced civic integration 66 policies perform better than immigrants with lesser developed civic integration policies (Goodman & Wright, 2015). The second inference of interest is the difference in policy impact between immigrants and non-immigrants; unlike the quasi-experimental design, regression analysis treats both groups as ‘intervention groups’, the reference point being native-born nationals. My study focuses on estimating both the group-level effect of mainstreaming on immigrants and the average policy impact of mainstreaming across the EU. It also focuses on comparing levels of policy exposure and policy effect for EU nationals and third-country nationals by using EU nationals as a reference point. My interest in group-level effects and in measuring the ‘gaps’ between immigrants and non-immigrants makes my study a natural candidate for ‘conventional’ regression analysis. It should be added that using a quasi-experimental method to measure mainstreaming is problematic. The method usually involves a single policy treatment that can be represented as a binary variable (treatment vs non-treatment). Labour market support policies, as it has been said earlier, vary in terms of coverage, scope, and duration. Focusing on a single treatment would leave most policy material uncovered, exclude a large number of countries from the analysis, and put the external validity of the study results under question (in other words, making a generalization from the findings of the study would be difficult). In addition to that, third-country nationals in Europe belong to multiple social, racial, religious and ethnic groups, which complicates the task of identifying a comparison group that would be as similar as possible to the treatment group in terms of pre-intervention characteristics. As a method of policy impact assessment, regression analysis has certain limitations. First of all, the estimation of a country-level effect is not particularly helpful in adjusting for 67 within-country variation. Some mainstreamed programs are implemented locally, not nationally (e.g. in Belgium, there are four regional public employment services instead of one national PES; in this case, I used aggregated scores). Second, under regression analysis, identifying a clear causal path between a policy and an outcome can be problematic due a number of potential confounding factors. This problem is to a certain part mitigated by adding control variables at the individual and country levels. The problem of country-level confounding is also addressed by using single-level models, which is discussed in the next few sections. On the other hand, the strength of my strategy is that it allows to make comparisons across countries based on average scores (e.g. the average policy effect score across the EU and the average group-level policy effect). Furthermore, regression analysis has a higher level of external validity: the inferences obtained from the survey sample can be generalized to other groups (third-country nationals and non-immigrants) in the EU. Finally, using EU nationals as a reference group is crucial for measuring the level of equality between immigrants and non-immigrants. Method of Cross-Validation In this study, I pursue a strategy of cross-validation: different statistical techniques or different pieces of data are used to validate the same type of relationship. The goal of the strategy is to verify the accuracy of the estimate. If findings obtained by different methods are consistent, this might indicate a pattern. First, the average impact of EU resident status on two types of outcomes, policy exposure and employment, is tested by cross-tabulation analysis and regression analysis, single-level and multilevel. In multilevel analysis, I look at the fixed-effects part. I examine if the outcomes obtained from a crosstab are similar to or at least follow the same trajectory as 68 regression model estimates. Second, at both levels, single level and multilevel, the relationship between policy exposure (PES registration) and labour market outcomes is measured by two sets of models. One of them tests the relationship between EU resident status and the odds of finding a job via PES, and another looks at the link between PES registration and finding a job via PES for each group of EU residents. Each method of quantitative analysis has its advantages and drawbacks. The strategy of cross-validation allows for mitigating the limitations of each method. In what follows, I will elaborate on how this strategy works for different types of regression analysis. Fixed-Effects Single-Level Regression and Random-Effects Multilevel Regression As it has been said, the study uses both fixed-effects single-level and random-effects multilevel regression analysis. The main and the most obvious reason for using multilevel modeling is that the data in the study have two-level structure with individuals nested in clusters (i.e. countries). Another reason is that this study proposes hypotheses about group-level effect. Multilevel modeling addresses the question of whether the differences in policy exposure and labour market outcomes among groups of EU residents are related to cluster characteristics, i.e. country-level variation in mainstreaming. The rationale behind the strategy of using both types of analysis, single-level and multilevel, is that one method balances the disadvantages of the other by producing different types of inferences. In the fixed-effects single-level models one can measure the average impact of employment support policies across the EU for different groups of EU residents. For example, one can estimate whether TCNs, on average, benefit more from registering with PES than EU nationals. While this is a valuable finding that tells us something about mainstreaming at work, 69 single-level regression does not account for country-specific distinctions. Under the fixed-effect single-level model we assume that the true effect size is the same in all groups (countries). The finding we obtain (e.g. the average impact of EU resident status) is the common effect size. The multilevel model focuses on how the effect size varies across EU countries. It acknowledges that individual observations are not completely independent of the country-level context since they are clustered. By partitioning within-group and between-group effects, a multilevel model captures both how observations vary within a cluster and how clusters deviate from each other. For example, multilevel regression can estimate whether under higher levels of mainstreaming (a country-level predictor) TCNs benefit more from employment support, both in absolute terms and relative to EU nationals. However, the efficiency of multilevel models is often questioned due to cluster confounding. In order to generate unbiased estimates in regression analysis, we must follow the assumption that predictors are not correlated with the error term. In multilevel models, the country-level variation might be a result of unobserved country-level confounders. This problem is partly mitigated by adding country-level controls but is not (and cannot be) completely resolved due to the very nature of the analysis. Fixed effects analysis can help eliminate bias by removing all variation between higher-level units (it is eliminated in coefficient estimation), including all potential unobserved confounding variables at the country level. This allows for a stronger causal argument. This approach, however, also eliminates country-level variation of interest. Using both single-level and multilevel models is a tradeoff between a precision of the estimate and the scope of the inference. 70 Explaining Bayesian Framework The use of Bayesian framework instead of more conventional maximum likelihood ‘frequentist’ approach (MLE) requires further clarification. Bayesian method is typically justified if a researcher uses informative ‘priors’ (i.e. the assessment of the probability of an outcome before the new data are collected) or a small number of groups or is interested in the parameters of effects rather than in parameter estimates. I use ‘flat’ (‘uniform’) non-informative priors. However, the number of groups (countries) in my study is limited to seventeen EU countries. I find the small number of groups and the interest in obtaining the true values of parameters to be a valid motivation for choosing the Bayesian framework. Whether Bayesian regression analysis should replace the MLE approach as a more accurate estimation strategy for multilevel modeling has been a subject of scholarly debate in social sciences for over a decade (Bryan & Jenkins, 2016, Elff et al., 2021; Maas & Hox, 2005; Stegmueller, 2013). In what follows, I will justify the use of Bayesian analysis in this study by referring to some major arguments and counterarguments used in the debate. I will specifically focus on the supposed advantage of the Bayesian approach in addressing the problem of cluster confounding in a sample with a small number of groups. Before proceeding to the justification, I start by discussing the similarities and differences between the two frameworks. It is well known that MLE is based on the sampling distribution of parameter estimates and their standard errors and confidence intervals. In Bayesian analysis, one specifies a ‘posterior model’ that describes the probability distribution of all model parameters conditional on two components: the ‘likelihood’ that includes information about the model parameters based on the observed data and a ‘prior’ that includes the information about model parameters which is obtained before observing the data. 71 The two approaches are based on different understandings of probability: in MLE statistics, probability is seen as an expected frequency of occurrence (the number of times the event occurs over the total number of trials); under this logic, only repeatable random effects can have probabilities and the sample size at both individual and group level must be sufficiently large (Maas & Hox, 2005). In Bayesian framework probability is defined as degree of belief (as a measure of how much we know about the event, i.e. any event we can observe), which in practice means that probabilities can be assigned to non-repeatable events and the small sample size at the group level does not yield biased estimates as in MLE models. Unlike MLE models, in which the exact sampling distribution is rarely known and is approximated by a large-sample normal distribution, Bayesian models use posterior distribution that can be estimated via MCMC (Markov chain Monte Carlo) sampling, without any large-sample approximation. Although Bayesian models provide summaries of posterior distributions of the parameters of interest across groups and ML models provide parameter estimates, MLE and Bayesian analysis are functionally similar in the sense that both yield the average beta-coefficient (e.g., for policy exposure and policy impact). An ML multilevel model and a Bayesian multilevel model with ‘flat’ non-informative priors will yield the same results, but only if the number of groups is big enough. In a sample where the number of countries is fewer than 20, cluster confounding becomes more pronounced in the MLE setting. It is noteworthy that both MLE and Bayesian models produce reliable, unbiased coefficient estimates for individual-level predictors even if the number of countries is as small as five. In models with a large number of individuals, individual-level covariates are unaffected by a small number of groups. The problem of cluster confounding becomes more pronounced when we test the impact of country-level variables on individual-level outcomes (e.g., the impact of 72 country-level mainstreaming on the labour market outcomes of immigrants) in a sample with a small number of groups. In this case, ML estimates are severely biased upward while Bayesian estimates are within +/- 5% of the true population parameter value. In his Monte Carlo study, Daniel Stegmueller (2013) showed that when using only a small number of countries (<10), Bayesian point estimates are biased at most 5%, whereas ML estimates reaches 10 – 15%. He concludes that Bayesian point estimates produce less bias if the number of countries is small and are virtually identical with ML estimates when the sample contains 25 or 30 countries. He suggests that the Bayesian method employs “excellent credible intervals” (Stegmueller 2013, p. 754). Bayesian confidence intervals (called ‘credible intervals’) are the quantiles from the full posterior distribution of the parameter. In other words, they provide the posterior probability that that the coefficient lies in this range. They are closer to their nominal level than ML confidence intervals and when they are biased, they are longer. In the MLE, the actual coverage rates of confidence intervals are too short and below the nominal level which might generate misleading results such as downward biased p-values. These conclusions are supported by multiple simulation studies (e.g. Bryan & Jenkins, 2016; Meuleman & Billiet, 2009; Hox et al., 2012). The above discussion does not suggest that ML multilevel modeling does not have any merits. Even the proponents of Bayesian methods agree that a frequentist random intercept model could be a relatively safe estimation strategy since it generates the estimates that are less biased than the estimates in cross-level interaction models (Stegmueller, 2013). Another alternative offered by frequentist scholars is a restricted ML estimator for variance parameters (Ellff et al., 2017). However, neither of these methods sufficiently addresses my research question which is focused on group-level effects. 73 To sum up, my overall methodological strategy of policy impact assessment is based on the method of cross-validation. By employing different methods to estimate the same type of relationship, my study pursues two goals. First, by comparing outcomes, I look for consistency across models. Second, using multiple methods helps mitigate their limitations. My study uses Bayesian multilevel regression analysis as a strategy that is better suited for samples with a small number of groups. In using this framework, I am looking to reduce the effect of cluster confounding and to improve the inferential accuracy of the models. 74 Chapter 4. The Index of Mainstreaming in Labour Market Support This chapter discusses the mainstreaming of labour market integration of immigrants in the EU by focusing on labour market support – a set of policy interventions designed to assist individuals who experience difficulties in the labour market. The chapter starts with a brief discussion of the scope of labour market support policies, their differences from other employment policy interventions, legal basis, funding sources, and the role of institutions responsible for policy implementation. It proceeds with explaining why labour market support exemplifies policy mainstreaming in the area of immigrant integration. To examine and categorize mainstreaming in labour market support across the EU, I have developed a policy index based on the data from the Eurostat Labour Market Policy database, the MIPEX, and administrative sources in each EU country. The chapter presents the Policy Index of Mainstreaming in Labour Market Support, discusses its dimensions as well as the policy patterns and cross-country variations the index reveals. The final section of the chapter examines how mainstreaming interacts with alternative integration indices and whether there is a relationship between mainstreaming and political behaviour. Labour Market Support in the EU Labour market support can be distinguished from other employment interventions in that its main goal is to address the needs of individuals who experience difficulties in the labour market: the long-term unemployed, employed individuals who are at risk of involuntary job loss, and inactive individuals who want to re-enter the labour market (for example, women re-joining the labour force after a family break). Many of these people face disadvantages and barriers that 75 may prevent them from obtaining employment due to factors such as age, health condition, disability, lack of suitable childcare, lack of work experience and skills. According to the Eurostat codebook, policy interventions in labour market support are classified by type of action. Table 5 shows three major types of policy intervention: services, measures and supports, which are subdivided into 9 categories.9 Table 5 Labour Market Support: Types of Action Labour market services (cat. 1) Labour market measures Training (cat. 2) Employment incentives (cat. 4) Supported employment and rehabilitation (cat. 5) Direct job creation (cat. 6) Start-up incentives (cat. 7) Labour market supports Out-of-work income maintenance and support (cat. 8) Early retirement (cat. 9) Mixed category (cat. 10) Labour market services include counselling and advisory services; the provision of relevant information about job vacancies and training; other types of assistance such as skills assessment, help with preparing CVs, etc. Labour market measures include policy interventions that (1) ‘activate’ the unemployed by providing them with new skills or work experience to improve their employability; (2) ‘activate’ employers by creating incentives for enterprises to hire people from vulnerable groups. Labour market supports cover financial assistance that 9 Category 3 (Job rotation and job sharing) is no longer used and is now included in Cat. 4. I have labelled the mixed category as category 10. 76 compensates individuals for job loss and supports them during job search (i.e. unemployment benefits) or facilitates early retirement. Finally, the mixed category covers policy interventions that contain components from more than one of the aforementioned categories. Labour market support in the EU is implemented by Public Employment Services (PES) – national employment services, their regional or local equivalents and other publicly funded organizations whose main function is to actively facilitate the integration of unemployed and other categories of jobseekers in the labour market10. To maximize the efficiency of the PES, the EU Parliament has established the European Network of Public Employment Services which encompasses all 28 EU states, Norway and Iceland. The Network was created for the period of June 2014 to 31 December 2020, followed by the Decision No 573/2014 of the European Parliament and the EU Council on enhanced cooperation between Public Employment Services. According to this document, PES play a central role in “contributing to the Europe 2020 Strategy of jobs and smart, sustainable and inclusive growth (‘Europe 2020’)”11. ‘Enhanced cooperation’ indicates, among other things, that to implement specific projects and joint initiatives (such as sharing best practices) developed by the Network, Member states have access to funding from the European Social Fund, the Regional Development Fund, ‘Horizon 2020 (The EU Framework Program the Progress and Innovation, 2014 – 2020), and the Progress/employment section of the EU Program for Employment and Social Innovation. Thus, labour market support policies in Europe have been funded by both national governments and the EU. This has affected not only the scope and the number of policy changes in labour market 10 With the exception of Belgium which has four regional PES instead of one national PES. 11 Decision No 573/2014/EU of the European Parliament and of the Council of 15 May 2014 on enhanced cooperation between Public Employment Services (PES), Official Journal of European Union, p. 1. 77 support but also their ‘quality’: policy interventions funded by the EU are usually the measures that have been recognized as ‘best practices’ in other EU member states. Labour Market Support as a Case of Policy Mainstreaming As defined in Chapter 2, policy mainstreaming in immigrant integration is an effort to address the needs of immigrants without directly targeting them through policies and programs that are designed for the general population. Peter Scholten and Ilona van Breugel (2018) have explained that “mainstreaming refers to an amalgam of efforts to abandon target-group-specific policy measures and to coordinate integration measures as an integral part of generic policies in domains such as education, housing and employment”. According to theorists of mainstreaming, policy mainstreaming can be direct and indirect. Indirect mainstreaming aims to address “problems that may be shared by different types of vulnerable groups – immigrants and others. These initiatives tend to target needs related to income, employment, education, neighbourhood, healthcare, gender or ethnic/racial background – rather than offering a policy intervention based on a person’s place of birth or immigration status.” (Collett & Petrovic, 2014), Health Action Zones (HAZs) in the UK are an example of indirect policy intervention. HAZs were community-based partnerships located in 26 areas of the UK and intended to reduce health inequalities in poor neighbourhoods inhabited by both immigrants and non-immigrants. Its three main goals were to re-organize and improve health and social services, empower deprived communities and resolve specific health-related problems such as high morbidity rate, teenage pregnancies and substance abuse. HAZs invested in a range of programs that promoted healthy lifestyles. Several HAZs (e.g. in Manchester, Salford and Trefford) enabled access to mental health services for young people of immigrant origin. 78 By contrast, direct approaches target immigrants as a group with specific needs within a wider range of mainstreamed policies. For example, in 2005, Denmark launched a policy of enabling youth to complete upper secondary education. In the city of Aarhus, this policy was linked to integration objectives: after language screening, children with Danish language support needs, most of them of immigrant origin, were referred to the schools outside their districts. The goal of the policy was to foster Danish language acquisition by placing immigrant children to schools outside their ethnocultural communities where the proportion of native Danish speakers was higher. Labour market support embraces both approaches. On the one hand, it exemplifies the indirect approach: immigrants are mainstreamed into policies that address the general population of jobseekers. Policy objectives in labour market support have increasingly focused on disadvantaged groups who are more likely to face barriers to employment – people with disabilities, seniors, women, ethnic minorities, individuals lacking relevant training. Immigrants can be a part of each of these vulnerable groups. Immigrants thereby are included into a different client group – the unemployed, and to a range of smaller client groups formed on the criteria other than residence status. At the same time, in some EU countries, labour market support contains policies that target immigrants as a separate group. Policy interventions include services that meet the specific needs of immigrants in individual projects or programs such as language courses, labour market orientation, social and cultural orientation and counselling. For example, in 2011, Sweden introduced a policy (‘Introductory activities for certain new arrivals’) aimed at facilitating the integration of refugees and asylum seeking immigrants into the Swedish labour market and society. Once an immigrant obtains his or her protection status, Swedish PES draws up an 79 individually tailored 2-year career plan set up together with the immigrant. The plan includes language training and social orientation as well as labour market activities. The Policy Index of Mainstreaming in Labour Market Support To examine and categorize mainstreaming in labour market integration across the EU, I have developed the the Policy Index of Mainstreaming in Labour Market Support (the MLMS), a policy index based on data from the Eurostat Labour Market Policy (LMP) database provided by the European Commission’s Directorate General for Employment, Social Affairs and Inclusion; the MIPEX; and administrative sources in each EU country. The index contains data about policy interventions aimed at facilitating the labour market integration of immigrants. The index includes data from 2006 to 2016 across 25 EU/EEA countries12; its statistical unit is public spending per policy category (e.g. “Training’). The index scores for 2016 are indicated in Table A6. The index has two dimensions: it measures the scope of mainstreaming in labour market support and the access to mainstreamed policies for immigrants. These two dimensions capture the essence of policy mainstreaming. Scope covers a variety of programs that address the needs of vulnerable groups, including immigrants; access measures what categories of immigrants are eligible to participate in these programs. 12 Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, the United Kingdom. 80 Access The levels of eligibility for different groups of immigrants vary considerably across EU countries. Some EU states provide equal access to PES services to EU nationals and any legal residents from outside the EU (e.g. Finland and Sweden). On the opposite side of the spectrum there are countries, such as Hungary, that restrict access to labour market support to citizens and permanent residents as well as (certain groups of) refugees13. Most EU countries require a work permit as a precondition for accessing employment services. However, some services are available for non-registered jobseekers which means that they may be available for any immigrant with legal status. Target groups in the LMP database are divided into two main categories: ‘registered unemployed’ and ‘other registered jobseekers’. Both categories refer to individuals who are registered with the Public Employment Services14. In the vast majority of cases, PES services are available for individuals who are officially registered with national employment agencies. As a result, the eligibility of labour market support is largely a function of whether immigrants qualify to register with the PES. Since there are no available data on PES registration in the LMP database, I used data from the MIPEX, qualitative reports from each EU country and other administrative sources (e.g. PES websites) to evaluate immigrants’ access to employment services. 13 In Cyprus, foreign nationals can get access to the national Labour Office only if they are political refugees recognized by the UN Commissioner for Refugees, are of Cypriot origin or married to Cypriot citizens. 14 ‘Registered unemployed’ are policy participants that are considered unemployed according to national definitions (there is no single definition shared by all EU states). ‘Other registered jobseekers’ refers to the number of entrants who are registered with the PES as jobseekers but who are not considered as registered unemployed according to national definitions (e.g. employed individuals who are at risk of involuntary job loss and therefore looking for a job change). There are other categories, such as ‘unemployed’/‘non-registered unemployed’ and ‘non-registered jobseekers’ that refer to the individuals who can have access to some labour market support services without registration. 81 In the Index, I have measured access by using a 0 – 4 scale where higher values indicate higher inclusiveness and lower values represent more restrictive rules towards immigrants. The Index evaluates the levels of access for four groups of third country nationals: (1) permanent residents; (2) residents on temporary work permits; (3) residents on family reunion permits who do not have permanent residency; (4) those with official refugee status. One point is added for each group of immigrants having access to PES services. For example, Sweden has a total score of 4 because all the four categories of immigrants have access to PES services. Latvia scores 3 because employment support is restricted to permanent residents, their spouses, and to refugees. Table 6 Access to Labour Market Support in the EU and Norway: Category Value Only EU nationals 0 Permanent residents 1 Residents on temporary work permits 1 Residents on family reunion permits 1 Refugees 1 Scope Types of Mainstreaming. Policy interventions in the Index have been divided into two categories: indirect and direct mainstreaming (or, targeting within mainstreaming). Indirect mainstreaming covers services accessible for immigrants as well as other groups. These policies 82 do not address immigrants as a group with specific needs, neither are immigrants indicated as targeted groups in policy descriptions. For example, ‘Support for training in institutions’ (Austria) provides subsidies covering up to 100% of expenditure spent on training courses organized by the educational institutions commissioned by the PES. The policy is targeted at ‘Registered unemployed (All)’, ‘Other registered jobseekers (All)’ and ‘Employed’. All these groups may involve immigrants, including individuals who have not been registered with the PES. Direct mainstreaming covers policy interventions that either address immigrants as the only targeted group (‘intensive targeting’) or involve targeting within a wider group of beneficiaries (‘some targeting’). For example, the ‘Portuguese for everyone’ program in Portugal is targeted at immigrants and ethnic minorities. It provides two training courses, elementary and advanced, and is run by PES. ‘Some targeting’ includes policies that officially include immigrants as a targeted group but it is unclear from policy description how exactly the policy addresses their specific needs and what these needs are. ‘Intensive targeting’ involves policies that clearly address immigrants’ specific needs such as language support needs (see Table A9). As opposed to the LMP database that covers all forms of policy support for the unemployed, the Index focuses on Active labour market policies (ALMPs) – the government programs that provide active support for labour market integration as opposed to the ‘passive’ policies that provide income replacement during periods of unemployment or job search. ALMPs include public employment services, such as disseminating information about available vacancies and providing assistance with interview skills and CVs; training such as classes and apprenticeships that help jobseekers increase their employability; employment subsidies to enterprises to hire new jobseekers. Passive measures are not integration policies per se because 83 they do not involve migrants’ proactive involvement in the process of adaptation to the host society. This is why I have excluded ‘Labour market supports’ – the policy action that covers unemployment benefits and early retirement pensions – from the Index. I have also excluded policy interventions called ‘Administration of LMP measures’ and similar administrative policy measures targeted at service providers. These are policy measures related to the implementation of LMP measures – the management and co-ordination of service providers, e.g. running costs of PES training centres. Сoding and Measurement. How can we measure policy scope? One way to do this is to simply count the number of interventions in each policy category. However, this method has its disadvantages. It does not account for policy scale: what qualifies as a ‘policy’ in the main data source – EU qualitative reports – can entail both a large-scale policy intervention and a more narrowly focused program, which makes policy comparison difficult. Instead, the Index measures public spending on labour market programs for each policy category (e.g., ‘Training’, ‘Employment incentives’). The indicator is measured as a percentage of GDP. This approach to measurement allows us to standardize country scores across the EU. ‘Scope’ is measured on a scale where the minimum total score is 0.02% of GDP (Estonia in 2007-2008) and maximum score is 1.65% (Denmark in 2014-2015). If a country scores under 0.55, I have categorized it as having a ‘weak’ level of labour market support. If it scores between 0.56 and 1.1, I have categorized it as ‘modest’. If a country scores between 1.2 and 1.65, it has a ‘strong’ level of labour market support. Each range, ‘weak’, ‘modest’ and ‘strong’, comprises a one-third part of the scale. ‘Access’ is measured on a scale of 0 to 4. If a country scores 0 – 1, it is categorized as having a ‘weak’ level of access to labour market support programs for immigrants. The score range between 1.5 and 3 is categorized as ‘modest’; and 3.5 – 4 points indicate a ‘strong’ level of 84 inclusiveness. Similarly to ‘scope’, each range in the access category is roughly a one-third part of the scale. The country score is the policy scope (i.e. the total number of public spending on the ‘active’ labour market policies) multiplied by the country’s access score. For example, in Hungary (2016) the access to labour market policies is restricted to three groups of immigrants: permanent residents, work permit holders, and refugees, i.e. Hungary’s access score is 3. The total expenditure on ALMPs in Hungary is 0.87, so the total country score is 0.87 x 3 = 2.61. This system of measurement allows to account for both the level of access to employment support for immigrants and the employment support policy scope. Mainstreaming in Labour Market Support: Policy Analysis This section discusses policy change in labour market support in each EU/EEA country over 10 years (2006 – 2016). It reports and discusses the most recent available average country scores and identifies the countries that are ranked highest and lowest in both dimensions of mainstreaming, access and scope. I proceed with examining the association between access and scope (e.g. if a country scores high in ‘access’, does it also score high in ‘scope’?). Finally, the section discusses variation in direct mainstreaming/targeted policies. Total policy scores can be misleading since they might indicate any combination of ‘scope’ and ‘access’ scores. A country scoring high in scope and low in access can have the same score as a country that is ranking low in scope and high in access. In what follows, I will therefore focus on each dimension separately. 85 Access: Policy Change and Average Scores Policy Change. As mentioned above, the ‘access’ dimension covers eligibility criteria for different groups of immigrants. As demonstrated in Figure 4, there has been policy consistency over time: with the exception of Hungary, most EU/EEA countries did not change their access regulations during this period. Until 2014, Hungary had highly restrictive access regulations towards immigrants. In 2014, Hungary adopted the Single Permit Directive that gave certain categories of immigrants (long-term residents, refugees, and work permit holders) access to public employment services. Average scores (2016). Most European countries (17 out of 25) have robust equal rights policies in the area of labour market support: they grant access to all categories of immigrants (Figure 5). Only two EU states, Czech Republic and Slovakia, restrict access to employment to EU nationals or EU nationals and permanent residents. Denmark and three Eastern European states, Hungary, Latvia, Lithuania and Slovenia, have restrictive access policies towards family reunion immigrants. Overall, ‘access’ is more stable than the second dimension – scope. Scope: Policy Change and Average Scores Policy change. In what follows, I discuss changes in the degree of policy scope over the period of 2006 – 2016. The scope dimension covers a variety of labour support programs that address the needs of vulnerable groups, including immigrants. In most European countries, policy change tends to be non-linear (e.g. Latvia, Sweden, Ireland) and varies within the same category (e.g. ‘modest’ policies remain ‘modest’ over time). However, there can be a minor (0.09 points) increase as in the case of Ireland 2009 – 2011. Three countries, Hungary, Germany, and Denmark, demonstrate radical policy change. In 2008-2010, Denmark increased public 86 spending on training, employment incentives and supported employment and rehabilitation. In Hungary, policy shift occurred due to a consistent increase in spending on supported employment in 2008 – 2016; spending on other programs remained unchanged. Germany decreased spending on start-up incentives, direct job creation and training and, to a lesser extent, on employment incentives; spending on supported employment remained virtually unchanged. Figure 4 Access in Labour Market Support. Policy Change in Europe: 2006 – 2016 Figure 5 Access in Labour Market Support. 2016 Figure 6 Scope in Labour Market Support. Policy Change in Europe: 2006 – 2016 Figure 7 Scope in Labour Market Support. 2016 87 Average score (2016). Most European countries do not have expansive labour market support policies: 17 countries score ‘weak’, 6 countries score ‘modest’, and only one country (Denmark) scores ‘high’. Denmark, Finland, and Sweden score highest – 1.62, 0.92 and 0.9, respectively; Latvia, Greece and Estonia are the countries with the lowest scores in labour market support. The data on the UK and Italy are missing. As Figure 8 shows, in 2006 – 2016, European states tended to invest more in ‘Training’ (35.65%); two other relatively well-funded policy areas were ‘Supported employment and rehabilitation’ (24.2%) and ‘Employment incentives’ (22.06%). On average, in 2006 – 2016 ‘Direct job creation’ received 14.22% of the overall spending on employment support. Start-up incentives received the least amount of public funding as compared to other employment support programs (3.87%). Figure 8 Public Spending on Labour Market Support. 2006 – 2016. Figure 9 Public Spending on Labour Market Support. 2016. Most countries that score low in ‘scope’ (i.e. countries that have minimal spending on labour market support) tend to have ‘modest’ or ‘high’ level of ‘access’ (i.e. to have highly 88 inclusive eligibility rules). We observe the same trend among countries that score ‘high’ in ‘scope’: most of them have ‘modest’ or ‘strong’ access. This is evidenced by the results from the following cross-tabulation (Table 7). The unit of analysis is country-year (e.g. Sweden 2007 or Belgium 2013), i.e. a score for each EU country in every year between 2006 and 2016. Among those countries that are ranking ‘weak’ in the scope dimension, 66% are ‘strong’ and 25% are ‘modest’ in the access dimension. We can observe a similar correlation among countries that score ‘strong’ in policy scope. In this group, there are no countries with ‘weak’ scope while most countries are ‘modest’ (89%) or ‘strong’ (11%) in ‘scope’. The results tell us that regardless of how much a country scores in scope, the level of access across the sample has been quite high. This has been a pattern for a decade. Table 7 Cross-Tabulation of Access and Scope in Mainstreaming Mainstreaming in labour market support: Access Mainstreaming in labour market support: Scope Weak Modest Strong Total Weak 17 9.50% 2 2.47% 0 0.00% 19 7.06% Modest 44 24.58% 6 7.41% 8 88.89% 58 21.56% Strong 118 65.92% 73 90.12% 1 11.11% 192 71.38% Total 179 100.00% 81 100.00% 9 100.00% 269 100.00% Pearson chi2(4) = 41.07 Pr = 0.00 89 Direct Mainstreaming Having discussed policy mainstreaming in general, I will now focus on a specific dimension of mainstreaming: direct mainstreaming, i.e. policies that target immigrants as a group. I will provide a descriptive overview of the policies before moving to in-depth case studies. The overview covers the most recent available data from 2016 (see Table A9). The scope of targeted labour market policies varies across EU countries and ranges from counselling to carefully tailored immigrant-oriented programs. Table A9 lists 36 targeted measures across the EU. The most widely implemented policy actions are ‘Labour market services’ and ‘Training’ (11 policy interventions in each category). The remaining policy actions include: ‘Employment incentives’ (7 policies), ‘Direct job creation’ and ‘Mixed’ (three policies in each category); and one targeted ‘Supported employment and rehabilitation’ program. In many cases, the immigrant-oriented policy measures target refugees as a separate disadvantaged group. This is consistent with the EU policy of putting PES at the forefront of the labour market integration of refugees, which has become a top priority for EU states during the 2013 – 2016 refugee crisis. According to the PES policy document ‘Labour Market Integration of Refugees’ – Key Considerations (European Network of Public Employment Services, 2018), the following four aspects are important for the labour market integration of refugees: (1) language training, ideally combined with work practice; (2) enhancing the skills and qualifications of migrants through the work-focused provision of training and education; (3) cooperation with partners and institutions; (4) cooperation with employers. These priorities are usually addressed by ‘Active labour market policies’ (ALMPs), i.e. the policies designed to improve job readiness (‘Training’) and increase employers’ motivation to hire individuals from disadvantaged groups (‘Employment incentives’). 90 Some PES have only recently adopted policies specifically targeted at refugees or at refugees and asylum seekers. Most of these policies are beneficial for both refugees and other groups of immigrants (e.g. family migrants), especially those who are low-skilled. For example, in 2014, Bulgaria launched the ‘Refugee and Training Program’ aimed to support the adaptation of refugees on the Bulgarian labour market. Its key activities include Bulgarian language training and new training and apprenticeship programs for jobseekers. The same year, Croatia started a new language training policy for refugees, ‘Learning Croatian’. Likewise, the ‘subsidy program for hiring beneficiaries of local interventions’ in Greece (started in 2014) provides financial incentives for employers to hire immigrant jobseekers who have received relevant training. ‘Reimbursement of employment contributions’ in Slovenia (started in 2011) has the same objective. A Swedish set of integration policies such as ‘introductory activities for certain new arrivals’ (since 2011), ‘supported work experience with a supervisor’ (since 2014), ‘practical foundation year’ (since 2014) entail services aimed at supporting refugees and asylum seekers. It is noteworthy that, unlike earlier policy measures that simply provided information to the unemployed (e.g. counselling or advisory services), almost all recently adopted programs (with the exception of ‘Introductory activities-2011’ in Sweden) are based on proactive efforts of policy actors, i.e. jobseekers and employers. They are focused on either ‘activating’ immigrants by improving their language and vocational skills or ‘activating’ employers by incentivizing them to hire immigrants. In other words, the new targeted policies have been designed according to the above-mentioned EU guidelines with their focus on ALMP. Such programs are categorized as ‘training’, ‘direct job creation’ or ‘employment incentives’. They coexist with and are layered over the ‘old’ immigrant-oriented programs and policies adopted by the EU states in the 1990s or early 2000s. 91 Sweden and Belgium have had targeted measures for much longer than the rest of the countries. Sweden has a long-standing experience and well-developed policies in the field of integration; new training-focused measures and employment incentives were adopted on top of the existing old ‘active’ measures such as ‘New start jobs’ which has been ongoing since 2007. Belgium also has both ‘old’ and ‘new’ active labour market policies (mostly regional) which can be explained by the long-standing high level of unemployment among immigrants in the country and the necessity to address this challenge through policy efforts. Mainstreaming, Political Culture, and Alternative Integration Frameworks After discussing what mainstreaming represents, this study moves to the reflection on what mainstreaming is not. Given a rich variety of integration frameworks and policy contexts in Europe, one can ask whether mainstreaming could be seen as an extension of a long-existing integration policy regime or as a by-product of a certain political culture. The question of interest is whether mainstreaming is indeed an independent policy strategy that does not follow the same trajectories as other approaches to integration. It is also important that we determine the extent to which mainstreaming is autonomous of present-day political cultures and climates. To explore these links, I examine whether a relationship exists between the two sets of integration policy indices: mainstreaming and multiculturalism, and mainstreaming and civic integration. In this section, I also look at the association between mainstreaming and political behaviour, specifically public support for right-wing, anti-immigrant political parties in Europe. As previously mentioned, the range of integration policy frameworks in the EU is not limited to the so-called national models. In the last two decades, scholars have established multiple typologies of integration and citizenship regimes that address this variety (Helbling and 92 Solano 2021). However, my study specifically focuses on multiculturalism and civic integration because, for decades, these policy frameworks have been among the most influential determinants of immigrant integration. Each framework established itself as a dominant cross-national pattern, albeit at different points in time. Multiculturalism was a part of the human-rights revolution that led to a greater commitment to acceptance of ethnic and cultural diversity across Western liberal democracies in the 1970s-mid-1990s (Kymlicka, 2012). In the 1980s, multiculturalism was institutionalized as a liberal alternative to assimilationism in many European states. Since the mid-1990s until now, there has been another widespread policy trend, civic integration, – a policy effort to make access to entry, settlement, and citizenship conditional on immigrants' economic self-reliance and their knowledge of the host country language, history, and culture (Goodman, 2015). Whether civic integration can be compatible with and layered on top of multiculturalism policies remains subject to academic debate; so does the narrative of the ''rise and fall'' of multiculturalism (Joppke, 2004; Kymlicka, 2010; Kymlicka & Banting, 2013). The discussion of mainstreaming can not be excluded from these debates because mainstreaming and the other policy frameworks reveal overlap and divergence across certain topics. Is deemphasizing race and ethnicity in mainstreaming similar to what we would expect to observe in some 'colour-blind' assimilationist models? Does the focus on economic self-sufficiency and human capital in mainstreaming to some extent mirror the emphasis on skills in civic integration? To address these questions, I quantitatively examine the relationships between the rival indices. One more relationship that matters is between mainstreaming and the underlying political culture. It is widely understood that cultural factors can shape public policy and that public policy, in turn, can influence political behaviour. For example, multiculturalism has been 93 associated with liberal ideas of tolerance and social justice and with political support from left-wing parties. By contrast, the 'civic turn' toward mandatory integration programmes gained excessive support from conservative, centre-right forces. Similar to multiculturalism, mainstreaming is a non-restrictive policy pattern but it also challenges liberal ideas of group-differentiated rights based on minority status. Where does mainstreaming fall of the political spectrum? Is it associated with a certain pattern in political behaviour? To address these questions, I examine the relationship between mainstreaming and public support for right-wing parties in the EU states. I graphed the policy variables in the scatterplots below. The analysis of mainstreaming is limited to the scope dimension and the total score of mainstreaming. The access dimension of mainstreaming is excluded due to the limited variation in values. Multiculturalism and Mainstreaming To study the relationship between multiculturalism and mainstreaming, I use the data from the latest available versions of the Multiculturalism Policy Index for Immigrant Minorities (2020) and of the MLMS Index (2016). The sample is restricted to 14 EU countries. The MCP Index measures the strength of multiculturalism policies across Western liberal states. For each of the three minority groups, immigrant groups, historic national minorities, and indigenous peoples, the MCP Index identifies a range of policies (e.g. ‘bilingual education’, ‘affirmative action’) that are characteristic of multiculturalism. The Index lists eight such policies for immigrant groups. Higher values on the 0 – 8 scale of the MCP Index indicate a greater level of acceptance of cultural accommodation by liberal states. 94 Figure 10 displays a zero slope which indicates that although there is a definite good-fit line for the data, the values of the Multiculturalism Policy Index are irrelevant to the values of the scope dimension of the MLMS Index: for each x-value, the y-value is the same. The Pearson correlation coefficient barely exceeds zero (r = 0.02) which suggests no association. Bivariate analysis summarized in Table A1 shows a slight (0.004) point increase in the scope of mainstreaming for each one-point increase in the MCP Index; however, with the p-value of 0.9, the estimate is inconclusive. Similarly, Figure 11 shows no correlation between the total score of mainstreaming and multiculturalism (r = 0.17); the two variables are positively related with a coefficient of 0.09 but have no statistically significant relationship (p = 0.6). Denmark is the only discrepant data point in both models: it scores low in multiculturalism (1 on a 0 – 8 scale) and high in the total score mainstreaming (4.86 on a 0 – 5 scale). I used Cook’s distance to measure how much influence the observations have on the predicted values. With scores of 0.7 and 0.9, Denmark exceeds the threshold point for both models (𝐷* = 0.3) which makes it an influential outlier (see Figure B1). To improve the accuracy of the models, I removed Denmark from the data. Figures 11 and 13 show how the regression line fits the data with the outlier removed. Without Denmark, mainstreaming and multiculturalism display positive correlation; however, the association is estimated as weak to moderate: r = 0.5/0.6. The relationship between multiculturalism and the scope of mainstreaming remains statistically insignificant (p = 0.08). One-point increase in multiculturalism is associated with, on average, 0.22-point increase in the total score of mainstreaming; the standard error of 0.1 indicates that the coefficient is 2.2 standard errors away from zero and therefore can be considered as quite precise. With the p-value of 0.051, this result is borderline significant (Table A1). 95 It can be concluded that the direction of the relationship between mainstreaming and multiculturalism is positive: the increase in MCP moves mainstreaming slightly upward. However, the relationship between the two frameworks is weak. Figure 10. Multiculturalism and the Scope of Mainstreaming, with Denmark Figure 11. Multiculturalism and the Scope of Mainstreaming, without Denmark Figure 12. Multiculturalism and the Total Score of Mainstreaming, with Denmark Figure 13. Multiculturalism and the Total Score of Mainstreaming, without Denmark 96 Civic Integration and Mainstreaming To examine the relationship between civic integration and mainstreaming, I use a sample of ten EU countries that combines the data from the most recent versions of CIVIX (2011) and of the MLMS Index (2016). CIVIX measures civic integration requirements in EU-15. On a 0 –6 scale, countries with higher values have ‘thicker’ citizenship requirements; countries with low scores have minimal requirements for obtaining status in a host country. As shown in Figure 14 and Figure 15, civic integration has a weak negative correlation with the scope of mainstreaming as well as with the total score of mainstreaming (r = - 0.4 in both cases): as the number and the scope of requirements for obtaining citizenship increase, the level of mainstreaming in labour market support decreases. On average, one-point change in the CIVIX score is associated with 0.13 decrease in mainstreaming but the relationship between the civic integration and mainstreaming is not statistically significant: the p-value of 0.22 tells us that there is a 22% chance we would find an effect of this magnitude in the sample if there was no relationship between the variables (Table A3). No influential outliers are detected. Support for the Far-Right and Mainstreaming I measure support for the far-right as a share of votes for the populist right-wing parties at parliamentary elections in EU states in 2014 – 2018 (see Table A4) to match the 2016 data from the MLMS Index. My sample covers the data on votes for 22 EU states. The correlation coefficient for both models, the one with the scope dimension and the one with the total score of mainstreaming, is 0.2 which indicates a weak positive association. Regression analysis confirms this result: as indicated in Table A3, a large p-value (p = 0.35, p = 0, 50) in both models suggests no relationship between the variables. Influential outliers are not detected (Figure B3). It can be 97 concluded that public support for the far-right in Europe is likely to have no effect on the level of mainstreaming. Figure 14 Civic Integration and the Scope of Mainstreaming Figure 15 Civic Integration and the Total Score of Mainstreaming Figure 16 Support for the Far-Right and the Scope of Mainstreaming Figure 17 Support for the Far-Right and the Total Score of Mainstreaming 98 Summary of Findings The Index and the analysis of interactions between mainstreaming and rival indices reveal a set of policy patterns. First, while most European countries have robust eligibility rules for immigrants (open policy access), their spending on labour market support is limited and in most cases does not exceed 3% of GDP (limited policy scope). Nordic countries – Denmark, Sweden, Finland – have the most generously funded employment support programs; Eastern European countries and Germany tend to have more limited spending. Second, according to the most recent available data, European governments tend to prioritize institutional and workplace training and support for apprenticeship over other employment support programs. Start-up incentives for the unemployed receive the least funding. Third, there is a considerable difference, both in policy scope and the level of access, between ‘Western’, old EU members and ‘the EU-10’, i.e. the new member states that joined the EU in 2004: the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia. Almost all of the EU-10 states (with the exception of Poland and Estonia) have poorly developed labour market support policies and restricted access to employment support. Slovenia has the lowest level of access to labour market support for immigrants in the EU (only EU/EEA nationals are eligible to register with the PES); Czech Republic, Latvia and Lithuania have restricted access to their employment support services. This trend can be confirmed by looking at other EU-10 countries that are not included into my analysis: Malta and Cyprus have restrictive regulations towards immigrants. 15 For many Eastern/Central European states, the labour market integration of immigrants has been fairly new to the policy agenda. The EU directives are a powerful factor in facilitating 15 There is no available data on spending for Malta, Cyprus, Bulgaria and Romania. 99 the labour market integration of immigrants (especially refugees) in Eastern European countries, through the joint initiatives of the EU PES Network, sharing best policy practices and access to funding. We have yet to see if ‘new’ (mostly Eastern European) EU states catch up with the rest of the EU. These expectations might be well grounded. The analysis of ‘direct’ mainstreaming policies indicates that there is a growing tendency toward expanding the policy scope in targeted labour market support in accordance with the EU directives on ‘activating’ employers and immigrants. This policy pattern demonstrates a growing role of inter-state cooperation in the EU; specifically, the role of the European Network of Public Employment Services and the states’ overall commitment to the Europe 2020 strategy. In order to test whether mainstreaming is an autonomous policy framework, this study examines whether between mainstreaming is correlated with alternative integration policy indices. I also explore the interaction between mainstreaming and political behaviour. This set of findings contributes to the debates on the national models of immigrant integration in several ways. First, the absence of correlation between mainstreaming and multiculturalism provides evidence for the claim that mainstreaming should be seen as a standalone policy pattern, not as an extension of long-existing, well-established policy frameworks such as national models. If the latter were true, we would expect to see a strong negative correlation between the Multiculturalism Policy Index and mainstreaming: increased scores in multiculturalism (i.e. a decrease in the ‘colour-blind’, ‘race-neutral’ approach characteristic of assimilationist models) leading to a decrease in mainstreaming. Second, the findings provide limited evidence that a more restrictive civic integration policy approach is associated with a lower level of mainstreaming. This finding is not 100 unexpected as these two approaches represent the opposite views of what integration entails. Civic integration involves targeting by immigrant minority status and places the onus of integration efforts on immigrants; higher scores indicate a ‘thicker’ citizenship content, i.e. heavy requirements for obtaining status. On the contrary, mainstreaming relies on the problem-based, non-group-specific approach, places the burden of integration on the state more than on immigrants, and is not offered as a mandatory requirement for obtaining citizenship. At the same time, both approaches emphasize the role of employment in immigrant integration, so there is a potential for positive relationship. Finally, we do not observe a relationship between mainstreaming and support for right-wing political parties. This finding supports the idea that although mainstreaming downplays the role of ethnicity and race similar to other integration strategies such as assimilationism (French republicanism), it is not associated with extreme political attitudes. 101 Chapter 5. Descriptive Case Studies and Mainstreamed Policies at Work This chapter offers three descriptive case studies that contextualize policy mainstreaming by looking at how the Index values translate into concrete policies and programs in country-specific contexts. As mentioned before, I have chosen cases that have high, medium, and low scores in both dimensions of the MLMS Index. Denmark has the highest score in the policy scope dimension of mainstreaming and a medium-high score in access, whereas France has only an average score for ‘scope’ but a high level of access. By contrast, at the bottom of the ladder Slovakia has only minimally developed employment support programs and a restrictive access policy. I will look at several contextual variables: the unemployment gaps between immigrants and non-immigrants, the labour market structure in each country, and the role of partisanship in politicizing both the labour market integration of immigrants. I will also analyze related policy frameworks such as employment protection and social security, i.e. unemployment benefits policy as a part of welfare systems. I will examine whether mainstreaming has been embedded into these frameworks and if so, to what extent. The final part of the chapter provides an illustration of how the causal mechanism of policy impact is expected to work in practice. The section focuses on mainstreamed policies in Denmark and France. Denmark Denmark has the highest policy scope score in the EU (1.62) and a medium-high (3) policy access score. In what follows, I provide more context for mainstreaming in Denmark. This case study starts with a demographic overview and proceeds with the analysis of Danish labour 102 market structure. I then discuss how mainstreaming is embedded in employment protection and social security frameworks as well as political background of integration policies. Denmark has a relatively small immigrant population. Since the end of World War2 the country has experienced several waves of immigration, with the largest migration flow resulting from the Syrian civil war (2015). Thus, while in the 1980s, the number of first and second-generation immigrants did not exceed three percent of the total population; by 2020, it had increased to 13.9 percent (Statista, 2022). The largest group of immigrants in Denmark is of Polish origin. The two largest groups of immigrants of non-Western origin are Syrians, Turks and Iraqis. Since 2015, Denmark has accepted more than 35, 000 asylum seekers from Syria in addition to immigrants from Africa (in particular from Morocco, Somalia, and Eritrea). With the growth of its immigrant population, labour market integration has become an increasingly politically salient issue in Denmark. Although the overall employment rate in Denmark is one of the highest in the world, there is a 20% gap between the employment rate of third-country nationals and native-born Danes (Ravn & Bredgaard, 2021). In 2021, only 66% of foreign-born residents of Denmark were employed (OECD 2022). Refugees and asylum seekers, especially those who are female, as well as immigrants of non-Western origin, have the lowest employment rates, which is consistent with trends across OECD countries (OECD, 2022). The problem of immigrant unemployment is exacerbated by the fact that the labour market in Denmark is very ‘flexible’: employers have a lot of freedom and latitude when it comes to hiring decisions (Bredgaard et al., 2006). Limited job security stemming from the ease with which Danish employers can hire and fire employees is compensated for by a high level of social security, i.e. a generous welfare system that guarantees 103 high unemployment benefits. This “flexecurity”16 (Bredgaard et al., 2006) might have resulted in more immigrants, especially family migrants and refugees, relying on welfare (Bredgaard & Thomsen, 2018). One more important factor is the demand for highly skilled workers in the Danish labour market which marginalizes jobseekers with low skills, including low-skilled foreigners (Brochmann & Hagelund, 2011, Ravn & Bredgaard, 2021). For over a decade, until 2011, the Danish immigration and integration policy framework could be characterized as targeted and centralized (Hedetoft 2006; Jorgensen 2014). In the 2000s, Denmark became an exemplar country for ‘restrictive’ civic integration and immigration policies referred to as “some of the strictest migration policies in Europe” (Bech et al. 2017, p. 6). This approach was likely affected by a change in government. In 2001, the coalition government formed by the centre-right Venstre party and the Conservative People’s Party came to power. This was the first time since 1924 elections the Social-Democratic party did not win seats. One of the key issues of the campaign was family reunification for immigrants which was seen by many voters as a way to bring spouses and children to the country and take advantage of relatively generous welfare benefits. As a response to this concern, in the 2000s, the right-leaning Danish government sought to restrict asylum migration and family reunification and provide incentives for increased high-skilled labour and international student migration. It also declared labour market participation as the main goal of integration. New immigrants were now held responsible for their economic success in the Danish labour market. In 2002, language tests were made prerequisites to acquire citizenship; in 2006, Denmark introduced civic tests. In 2006, Denmark introduced integration contracts which conceived of integration as a two-way process: immigrants were argued to have both rights and duties including the obligation to integrate. The 16 The term was coined by the Prime Minister of Denmark, Poul Nyrop Rasmussen in the 1990s. 104 integration contracts offered a mixture of sanctions and rewards, permanent residency status being the main incentive for greater involvement in integration process and limited access to naturalization being the sanction. Furthermore, there was an implicit demand for sociocultural assimilation which was a subject of public debate about cultural values. The integration contract (Integrationskontrakt) is a three-year integration program which is offered to newly arrived immigrants. The program includes Danish language instruction, courses on social and work conditions in Denmark, and a course on Danish culture, history, and core values17. This is a binding contract between an immigrant and the municipal authorities containing an integration plan for the three years with a particular focus on finding employment in Denmark. In 2001, the government established the Ministry of Refugees, Immigrants and Integration (IMN). The goal was to gather all integration-related issues under one political authority. IMN was in charge of social issues previously overseen by eight separate ministries including the Ministry of Justice (responsible for naturalization), the Ministry of the Interior (integration), the Ministry of Housing and Urban Affairs (segregation and gentrification), and the Ministry of Financial Affairs (integration in workplaces) (Jorgensen, 2014). The government took a centralized approach to integration under which IMN addressed immigrants’ needs in various areas by consolidating policy efforts on immigrants as a solidly distinct social group. 17 Upon signing the integration contract, the immigrant had to also sign ‘a Declaration on Integration and Active Citizenship in Danish Society’. The goal of the declaration was “to make the values of Danish society visible to the individual foreigner”. The appreciation of Danish values, as defined by the Ministry of Refugees, Immigrants and Integration, includes willingness to “respect Danish legislation and protect Danish democratic principles, learn Danish and acquire knowledge about Danish society, be self-supportive, be aware that it is illegal to use force or violence toward one’s spouse or children, respect personal freedom and integrity, freedom of belief and expression as fundamental rights in Denmark, be aware that discrimination on the basis of gender, colour, or religion is illegal, and recognize that Danish society is against terrorism” (Jensen et al. 2009, p. 7). 105 While in early 2000s, there had been a cross-partisan consensus on the restrictive policy turn this changed in in 2011 with the victory of the Social Democrats’ coalition with the Socialist People’s Party and the Social Liberal Party. The new government dismantled IMN and divided its competencies across four ministries: the Ministry of Justice, the Ministry of Employment, the Ministry of Children and Education, and the Ministry of Social Affairs. Each ministry was made responsible for embedding integration-related work into their general activity. The Ministry of Children and Education is in charge of teaching Danish as a second language, among other things; the Ministry of Social Affairs supervises social and political integration including prevention of radicalization; the Ministry of Justice deals with matters related to asylum, visa and family reunification; the Ministry of Employment is responsible for the integration of immigrants in the labour market and educational system. The Ministry of Employment’s Public Employment Services became one of the main actors engaged in the integration process. This step was politically motivated as it marked a move away from previous ‘harsh’ integration policy and rhetoric without shattering the entire policy framework with its focus on economic self-reliance and education. As Martin Bak Jorgensen puts it, “INM was symbolic of a particularly restrictive policy direction that the parties wanted to move away from” (Jorgensen 2014, p. 5). The parties that constituted the new left-leaning government had long been critical of the existing rhetoric of immigration which they characterized as divisive. However, the purpose of the policy shift was not purely symbolic. The new government sought to maintain its commitment to the long-standing integration goals by experimenting with new strategies such as mainstreaming. The restructuring of IMN can be viewed as a step towards mainstreaming immigrant integration into multiple areas: employment, education, social and political participation. 106 The Danish government has focused its effort to reach people with migration background through strategies that also target the general population – a policy approach which continues under the current government led by the Social Democrats. People having a refugee status in Denmark are eligible for a three-year integration program but also have access to PES services; all other immigrants fall under the same ‘regular’ PES programs as Danish nationals. In the area of labour market support, Denmark has focused on the active labour market policies rather than ‘passive’ measures such as benefits and allowances. In 2008, after the global financial crisis during which the employment gap between foreigners and native-born Danes started to rise, Denmark increased public spending on the ALMP such as training, apprenticeship and supported employment and rehabilitation. Examples of these policies are available in Table A10. It is the ALMP that provide Denmark’s high scope score in the MLMS index. It is remarkable that the main focus has been on activating jobseekers rather than employers. PES activate jobseekers by providing them with new skills through vocational training programs including Danish language courses for immigrants. This approach correlates with the fact that in labour markets with low employment protection, higher amounts of human capital allow workers to switch employers more easily. PES in Denmark does activate employers by offering them subsidies for hiring PES-trained jobseekers from vulnerable groups but the number of such programs is relatively small. This policy preference is correlated with the European idea that labour market integration is a two-way street: state-provided benefits and obligations to integrate must be balanced. If a jobseeker registered with PES fails to find employment within four years, they lose access to unemployment benefits and must apply for means-tested social assistance provided by local municipalities. The responsibility for the jobseeker passes from the PES to the municipality. 107 As a concept, mainstreaming has been rarely used in the Danish public discourse on immigration but the idea of embedding integration into ‘regular’ policy operations has become a trend across policy areas. The turn towards mainstreaming is based on the conviction that specific integration problems such as unemployment should be handled by government agencies that do day-to-day work on similar issues across the general population. Mainstreaming should not be seen as a liberalizing policy trend: in Denmark, it coexists with ‘restrictive’ civic integration policies such as integration contracts. In Denmark, it marks the search for new means of achieving the same long-standing integration goal rather than the move towards a new goal. France France grants access to employment services to all the four categories of non-EU newcomers, therefore scoring 4/4 in the ‘access’ category. However, at the level of scope, its labour market support policies are at the medium level of 0.71. The case study explains these indicators by outlining the overall context of mainstreaming in France: the demography of immigrants, the labour market structure and the political context in which policy is made. France has attracted immigrants since the middle of the 19th century. The first arrivals were from Europe (mostly from Poland, Spain and Italy), followed by immigrants from the former French colonies in North Africa and the Middle East. France has been home to one of the largest populations of ethnic minorities in the EU. In 2020, the number of people living on French territory who were born abroad was 8.5 million (Statista, 2022). Algeria, Morocco, and Tunisia have been the top three nationalities of third-country nationals. In France, labour market participation varies between immigrants and non-immigrants. In 2019, people of non-immigrant origin had an employment activity rate of 67.5 percent compared 108 to a 58 percent rate for descendants of immigrants (Statista, 2022). However, those immigrants who have been in France for more than ten years, have more favourable employment outcomes than native-born men of similar age and educational background. This is true for many origin countries, including those from outside the OECD (OECD 2022). Not surprisingly, high-skilled immigrants have a higher employment rate than low-skilled immigrants. As in Denmark, women and those who arrived through family reunification display lower employment rates; however, the reason is inactivity rather than unemployment. Women tend to stay inactive as compared to men; men are not only more likely than women to lose employment but also to subsequently move back to employment (Simon & Steichen, 2014). Unlike in Denmark, where the labour market is deregulated, the labour market in France is heavily controlled by the state. For example, employers have limited freedom to fire employees. as French governments have exercised control over the so-called ‘dismissals on economic grounds’, i.e. when employees are dismissed for reasons unrelated to their behaviour at work or work performance. In this case, a dismissal can be the subject of negotiations with direct involvement of the Ministry of Labour, the Labour Tribunals and labour unions. France is among the European states with the strongest job security laws which are intended to reduce unemployment by reducing employment loss18. High employment protection legislation leads to low labour market mobility. However, such policy can have a negative impact on vulnerable groups such as immigrants: since the laws make it costly for employers to dismiss workers, many 18 There is a vast literature that argues that countries with higher levels of employment protection display worse labour market performance and that this effect is particularly detrimental for vulnerable groups. According to MIPEX, France “delays and discourages the labour market mobility of non-EU immigrants more than most Western European/OECD countries” (MIPEX 2022). The rigidness of the labour market for immigrants can be seen as a part of the overall lack of labour market mobility. According to OECD, in France, “the strong protection accorded by open-ended contracts hinders labour mobility” (Vlandas 2017). 109 employers avoid hiring people they perceive as “not a safe bet”. Another strategy is to offer short-term contract jobs that require a short-time commitment on the part of the employers: in France, between 2000 and 2017, the number of temporary jobs with a duration of less than one month increased by 165% (Gazier, 2019). As a result, many foreigners are trapped in precarious employment situations. The Bismarckian welfare system19 in France imposes strict conditions on immigrants: unemployment benefits are either earnings-related, i.e. financed by previous social contributions, or tax-financed, i.e. granted at a flat rate based on previous work duration. In both cases, to be eligible for the benefits, immigrants must have previous work experience, and be looking for work. Whether French integration policies address the immigrant employment problems stemming from the rigid labour market structure is discussed below. The French policy framework of integration has long been characterized as ‘assimilationist’. One of its core elements is the “colour-blind” approach to immigrant integration. After passing the antidiscrimination law in 1972 and becoming party to the International Convention Against All Forms of Racial Discrimination in 1971, France committed to protecting ethnic minorities from injustice. However, the state does not go beyond the minimal protection of minority rights. As Erik Bleich has put it, “there is a normative consensus in France that affirmative action or any race-conscious policy is anathema to French values” (Bleich, 2001, p. 271). The Republican ideal of egalité – equality of people before the law regardless of their origins, race or religion, excludes any possibility of granting differentiated rights for minority 19 The so-called Bismarckian or ‘conservative corporatist’ welfare states include the six founding EU states of Germany, France, Italy and the Benelux countries (Belgium, the Netherlands, Luxembourg); the Visegrad countries (the Czech Republic, Hungary, Poland and Slovakia); and Switzerland. Under Bismarckian welfare regime, social protection is determined by a beneficiary’s labour market performance and employment situation (Palier 2007). 110 social groups. French political culture does not embrace the ‘multiculturalist’ idea of enhancing equality by giving preference or special consideration to underrepresented groups. By the same token, the Republican idea of laicite (secularism) forbids the wearing of religious symbols in public – a policy that disproportionately affects Muslim immigrants. Although the French integration framework goes beyond Republicanism and clearly includes other indicators, even the critics of ‘integration models’ acknowledge that the French do not recognize ethnic or racial groups at policy level (Bertossi, 2011, p. 1565). How is the ‘colour-blind’ French policy approach related to mainstreaming? As Escafré-Dublet (2014) puts it, in France, “there is a general distrust of policies that target a particular group over others” (p. 1). When it is applied, policy targeting is based on the principle of citizenship, not on race or ethnicity. In other words, French policies target everybody who has French passport including descendants of immigrants. All integration efforts are limited to a third-country national’s first five years in France. After this time period, immigrants’ needs are addressed through general policies; this implies mainstreaming. For example, immigrants’ needs are addressed through the area-based policies aimed to address social inequality in problematic neighbourhoods and localities (Escafré-Dublet, 2014). These short-term integration programs are under the jurisdiction of the Interior Ministry. It is noteworthy that, in the 2000s, France experienced the same institutional change as Denmark. In previous decades, from 1965 to 2007, integration had been under the jurisdiction of the Social Affairs Ministry. In 2007, under the centrist-conservative Republican government led by Nicholas Sarkozy, France created a controversial Ministry of Immigration and National Identity that combined the competencies of three ministries: Foreign Affairs, Interior and Social Affairs. Among other things, the Ministry was responsible for “the policy of memory and the 111 promotion of Republican principles and values”. The new Ministry was one of Sarkozy’s election pledges. As in Denmark, this was an attempt to centralize policy efforts in the area of immigrant integration as part of the overall targeted restrictive approach. Under the same approach, the 2006 law on immigration (Loi Sarkozy) abolished the ‘automatic’ regularization of undocumented immigrants after 10 years’ residence in France. Two provisions for entrance were introduced: the proof of financial self-sufficiency which would reduce immigrants’ eligibility for welfare benefits, and the mandatory ‘Reception and Integration Contract’ in which the immigrant committed to learn French and respect French values. The role of immigrants’ employment was emphasized in a set of statements in which Sarkozy stated his preference for ‘selective’ high-skilled labour migration over ‘imposed’ family migration – the same policy trend we observe in Denmark (Marthaler, 2008). The new Ministry only lasted for 3 years. The creation of the Ministry of Immigration and National Identity prompted protests from the left, mass media and from public intellectuals who claimed that it stigmatized foreigners and French descendants of immigrants by suggesting that they are a threat to the French national identity. Many of them considered the initiative to create a new ministry as a populist tool Sarkozy used during his presidential campaign to win over far-right voters from the National Front (France’s Sarkozy Attacked, 2007). The activity of the Ministry was opposed by the Socialist Party, migrants’ associations, the Christian Democratic Union for French Democracy, the Catholic Church and influential non-governmental organizations such as the Education Without Borders Network and the Movement Against Racism and for Friendship Between Peoples. After such pressure, in 2010 Nicolas Sarkozy officially apologized for his stance on French national identity and made a dramatic policy U-turn by bringing immigrant integration 112 back under the responsibility of the Interior Ministry. In 2012, Interior Minister Manuel Valls announced his Ministry’s commitment to the reception of newcomers and to disperse immigrant integration agenda across various departments. The new (or the ‘old new’) policy intended to address immigrants’ needs through the policies that target the general population. In France, mainstreaming is not a new policy trend. Apart from the three-year policy change discussed above, mainstreaming has been an integral part of the ‘Republican’ approach to integration for decades, starting in the post-WWII years. Political actors and government ministries have avoided targeting immigrants and minority groups since it is believed to be inconsistent with the French principle of equal treatment (Escarfe-Dublet, 2014). The needs of minority groups have therefore been addressed through general policies. By the same token, Sarkozy’s policy initiative to create a new ministry received such a backlash because it was perceived as a targeted approach that marginalized immigrants and questioned their ‘Frenchness’. Having mainstreaming in immigrant integration as a long-standing, ‘traditional’ policy approach does not ensure expansive, generously funded labour market supports. Although France grants access to employment services to all four categories of non-EU newcomers, its spending on labour market support programs does not exceed the EU average. France’s ‘colour-blind’ approach ensures that immigrants get the ‘average’ policy exposure similar to other vulnerable groups, with little to no targeted measures. Unlike the UK or Nordic countries such as Denmark, France does not have a long policy history in the area of employment support. In the 1990s, France prioritized job creation programs that ‘activated’ employers by incentivizing them to hire workers from underprivileged groups such as the long-term unemployed, social assistance recipients, younger and older workers; 113 public spending on this type of policy continued to rise in the next decade. The state intended to reduce employers’ social expenditures (e.g. the exemption of Social Security contributions) and finances subsidized contracts. Policies that ‘activated’ jobseekers such as training and start-up incentives were only introduced in the 2000s, although the funding of training remained limited as compared to direct job creation (Coquet, 2015). The policy of subsidized job schemes (which are not long-term arrangements) is likely to contribute to a growing number of short-term contracts the labour market in France has been inflated with which are, in turn, a reflection of a high-level employment security system. In addition to that, as a response to the financial crisis of 2008, the government unified the French Public Employment Services (ANPE) and the unemployment insurance system into a single government agency, Pole Emploi. The goal was to create a one-stop shop for jobseekers and increase the overall effectiveness of labour market support but also to reduce the costs of PES. As a result of this merging, unemployment assistance benefits were reduced to give way to activation policies mentioned above. As immigrant integration is mainstreamed into general policies, it becomes part of the policy regime where the best funded labour market support programs, such as subsidized job creation, complement and support the existing employment security policy framework. In this framework, the state plays a dominant role as it can ‘punish’ and ‘reward’ employers for their hiring and firing decisions. The labour market support policy thus reinforces the dual character of the French labour market with its privileged, primary permanent employment segment and the secondary sector of atypical, short-term jobs. Next, in the 2000-2010s, there has clearly been a policy shift towards activating jobseekers through policies such as training and apprenticeship. However, under this policy approach, immigrant jobseekers are still expected to have previous work experience which does not cater the needs of ‘inactive’ immigrant jobseekers (i.e. those 114 who did not have employment in France and are not looking for it) many of which are female family migrants. Employment support thus correlates with the French welfare system that relies on previous social contributions such as professional record. Slovakia The Slovak Republic has restrictive policies in terms of both access and scope. Its ‘scope’ score is one of the lowest in the MLMS index (0.21); the country provides zero access to labour market support to foreigners. In this section, I situate mainstreaming in a wider country-specific demographic and policy context. Until the Russian invasion of Ukraine, Slovakia was not perceived as a traditional destination country for immigrants. It is a relatively ethnically homogeneous society, one of the top immigrant-sending countries in Europe. Since February 2022, Slovakia has been a haven for refugees from Ukraine. (Hutt, 2022). Until then, the overall number of foreigners in population barely exceeded 3 percent, which is the third lowest figure in the EU although this number has been slowly increasing since 2004. The most numerous group of foreigners is formed by nationals of the neighbouring Slavic states: Ukraine, Serbia, Russia, Czech Republic, and Poland who come to Slovakia to work, study or join family. Non-European immigrants (mostly from China, Korea, Vietnam) are a comparatively tiny group that forms together 7 percent of all foreigners in the country (IOM 2022). Since the representation of foreigners remained low until February 2022, the problem of migrant unemployment in Slovakia was less salient than in Western European states. The country has one of the lowest rates of highly skilled immigrants arriving from outside the EU: in 2016 – 2019, a total of 61 foreigners arrived under the Blue Card Program. 115 Most immigrants to Slovakia are young males between 16 and 24 years old attracted by temporary job opportunities in car manufacturing, the largest industry in Slovakia (Guzi & Fabo, 2020). Overall, migrant workers are successful in the labour market. The employment rate among first-generation immigrants is 3.2 percentage points higher than among natives (Kananec & Sedlakova, 2016). At the same time, the employment rate of immigrants depends on the country of origin. EEA nationals are older and have higher earnings as compared to native Slovaks. Third-country nationals earn less than Slovak nationals except for young people under 30 (which accounts for the majority of foreigners), whose earnings exceed the salaries of young Slovaks of the same age group. Many young Slovaks seek employment abroad and their positions are filled by young third-country nationals (Guzi & Fabo, 2020). The government, however, seeks to fill labour market gaps with domestic workers first (Kananec & Sedlakova 2016, p. 16). Labour market regulation in Slovakia does not follow a consistent pattern: the labour market shifts between liberalization and deliberalization according to the alternation of power between left-leaning and right-leaning governments. While conservative governments are oriented towards a more flexible, deregulated labour market, leftist governments tend to lean towards providing stronger state protection of workers by strengthening the position of trade unions. The inconsistency of Slovak labour market regulation goes hand in hand with the ambiguous character of the welfare system which has a Bismarckian orientation but still allows for some degree of universality in terms of access to certain types of benefits. Similar to France, welfare benefits are funded by social insurance contributions, are earnings-related and granted on the basis on previous employment record. However, since 2015, the Slovak Constitution has guaranteed minimum pension for retirees with minimal employment record (Cerami, 2021). 116 Foreigners living in Slovakia are entitled to receive unemployment benefits under the same conditions as Slovak nationals, i.e. the benefits are granted to the individuals who have paid unemployment insurance contributions for at least 2 years prior to their registration as jobseekers. In other words, as in France, benefits are based on professional record. As I mentioned above, migrant workers on average have higher employment rates than native Slovaks, with only a minimal number of migrants applying for unemployment benefits. In 2011, the Slovak government adopted a new document “The Migration Policy of the Slovak Republic – Perspective until the year 2020”. The document prioritized high-skilled labour migration from ‘culturally similar’ countries. A decade later, the policy directive is the same: the website of the Ministry of Labour, Social Affairs and Family states that Slovakia focuses on the support of a highly-skilled third-country labour force. This involves the immigration of students, scientific and research workers, artists and “foreign Slovaks”, i.e. successful emigrants of Slovak origin (Ministry of Labour of the Slovak Republic, 2022). Overall, the Slovak Republic is characterized by unfavourable integration policies. MIPEX classified the Slovak approach to immigrant integration as “equality on paper” only (Solano & Huddleston, 2022). In 2009, the country adopted the “Strategy on the Integration of Foreigners in the Slovak Republic” which was followed by implementations reports. NGOs working with migrants have criticized that the reports are vague and lack statistical data (Kodaj & Dubova, 2013). Third-country nationals enjoy basic rights but face multiple barriers to employment. This applies in particular to asylum seekers – a tiny portion of immigrants to Slovakia. According to European Commission policy reports, “access by asylum applicants and asylum and subsidiary protection holders to the labour market is difficult and lengthy, and refugees must overcome multiple legal and practical constraints, unequal treatment and 117 discrimination” (Fajnorová & Chaloupková, 2021). Restricted access of foreigners to employment support is consistent with the general approach to integration. In terms of labour market mobility for immigrants, Slovakia ranks second last among MIPEX countries which, as in France, could be a part of the overall pattern of having low labour market mobility in the country. The low labour market mobility is strongly related to the problem of segmented housing market: Slovaks who live in more deprived rural areas cannot afford buying or even renting an apartment in major cities with more job opportunities and prefer to stay put (Kananec & Sedlakova 2016). Labour market support policies seek to address the problem of low labour mobility by offering a moving and a commuting allowance that support unemployed Slovak jobseekers who permanently moved to a different part of the country for a job opportunities post-unemployment. Slovakia tends to spend more on passive measures such as allowances than on active labour market policies. Overall, Slovakia has had one of the smallest expenditures on employment support among European countries, which is reflected in the low score at the level of ‘scope’ in the MLMS index. Furthermore, employment support is characterized by its limited capacity to address the needs of vulnerable groups such as low-skilled workers, immigrants and the Roma minority. As indicated in Table A12, PES do not provide any Slovak language assistance to ethnic minorities. The lack of targeted measures does not indicate a policy shift towards mainstreaming. Rather, it it a reflection of a bigger pattern – overall minimal investment in the labour market integration of immigrants. Slovakia, a country with an exceptionally small number of immigrants, most of whom participate in the labour market, does not seem to prioritize labour market integration and to look for new policy mechanisms to achieve the best outcomes in this 118 area. Immigrant demography and immigrant employment rate tend to be more important determinants in the context of mainstreaming than policy factors such as employment protection and social security. The Mechanism of Policy Impact: Mainstreamed Policies at Work This section builds on the case study analysis in the previous sections as it shows mainstreamed policies at work in country-specific settings. The purpose of the section is to support the main argument by focusing on how exactly mainstreamed policies correlate with employment outcomes. I will use examples of mainstreamed policies in Denmark and France, two European countries with rich labour market support programs, and refer to the decisions and procedures that are shared by all European PES. This section does not propose a causal analysis; rather, it supports the quantitative analysis by offering an empirically grounded insight into the mechanism of policy treatment specified in Chapter 2. As explained in Chapter 2, the mechanism of policy impact works as follows: higher scores in the ‘access’ and ‘scope’ dimensions of mainstreaming are likely to account for higher policy exposure; the increase in exposure is conducive to higher employment outcomes. The link between the access dimension of mainstreaming and higher exposure is quite straightforward and does not require a detailed explanation: the more categories of immigrants have access to employment support programs, the more they will participate in them. This section will therefore focus on the relationship between the scope dimension of mainstreaming and labour market outcomes. As previously mentioned, ‘scope’ is measured as the amount of government spending on each labour market support policy. The mechanism of policy impact is based on the assumption 119 that more generous state funding is likely to lead to the expansion and development of employment support programs which, in turn, leads to higher policy exposure. The expansion and development of employment support programs mostly involves Active Labour Market Policies (ALMPs). ALMP are the policies measured in the Index of Mainstreaming in Labour Market Support: they entail new training courses, apprenticeship programs, state-funded wage subsidies for employers, etc (see Table 5). The allocation and implementation of ALMP is a core responsibility of all European PES and a core part of the PES budget (Peters 2018, p. 14). According to PES policy assessment documents, on average, most of the PES budget in the EU (60%) is spent on ALMP. Almost 30% is spent on staff costs, 0.3% on staff training, and 13% is spent on other types of expenditure. (Peters, 2018). An example of the expansion of ALMP as a result of increased spending is Denmark after the global financial crisis of 2008. When the Danish unemployment rate increased from 2.6% in 2008 to 6.6% in 2010, Denmark started to invest more in labour market support: the overall government spending rose from 3.18% of GDP in 2008 to 4.89% in 2010. The increased spending resulted in further expansion of ALMP. For example, during 2009 and the first part of 2010, Denmark introduced new ALMP measures intended to combat youth unemployment. The government implemented two sets of measures, both targeted at young people of immigrant and non-immigrant origin who were the first to be affected by the financial crisis. One set of measures offered apprenticeship contracts and training courses. Another entailed a combination of training and subsidized employment arrangements: the state provided a training subsidy to businesses that hired unemployed youngsters upon completion of on-the-job training. Conversely, reduction in funding is likely to lead to the decrease in ALMP performance. For example, in 2005, under the French law on social cohesion (Loi de programmation pour le 120 cohesion sociale) which was intended to combat structural unemployment and social exclusion of vulnerable groups, the funding of subsidized jobs was reduced. Under ‘subsidized employment’, an employer receives a wage subsidy from the state for hiring a jobseeker from a vulnerable group. The reduction of wage subsidies led to the decrease in job duration and in the length of contracts because the state was no longer able to subsidize long-term employment (Coquet, 2015). While short-term contracts can be of interest for certain groups of jobseekers, most unemployed individuals, including immigrants, look for a more stable employment, which makes a policy shift from long-term contracts towards short-term employment an example of decreased ALMP performance. One may argue that earmarking does not necessarily result in the appropriate use of government funds. However, PES have a high level of internal and external accountability and budget transparency. As far as policy performance is concerned, national PES are accountable not only to their governments but also to the European Commission PES Network, specifically to the PES Benchmarking Group that started work in 2002 with the support of Austrian PES. The Group is in charge of ‘benchmarking’, an indicator-based assessment system of PES performance which is used across the EU. The purpose of policy assessment is to identify ‘best practices’ among national programs and use these findings for evidence-informed institutional learning from peers (‘benchlearning’). Most importantly, one of the indicators of PES performance assessment is ‘allocation of PES resources’, which implies financial accountability. Under ‘benchmarking’, each national PES conducts a self-assessment which is then double-verified during external assessment provided by the European Commission experts (Fuchs et al., 2021). Such a robust level of external control significantly reduces the chances of the inappropriate use of government funds. 121 Next, how exactly does the expansion and development of labour market support programs affect policy exposure? In what follows, I will elaborate on the link between the two variables. Third-country nationals in the EU get in contact with Public Employment Services quite early upon their arrival in the host country for immigration-related paperwork and procedures. According to EU policy documents, “PES play a limited role in implementing labour migration policies and this is centred mainly on administering labour market tests and work permits” (Molnár et al., 2020, p. 34). In countries such as Germany, employment-based residence permits are conditional on labour market tests administered by PES. In countries such as Austria, Belgium, Cyprus, Lithuania, Netherlands, Slovenia, PES are in charge of not only labour market tests but also of the entire work permit procedure. In general, PES are responsible for informing immigrants about their job search strategies, including the services provided by PES themselves. Contacting PES for immigration paperwork might increase immigrants’ awareness of employment support programs offered by PES. However, it does not necessarily translate into policy exposure, i.e. the actual enrolment in PES employment support programs. Participation in such programs requires considerable commitment of time and effort: their duration varies from several months to several years. Signing a contract with PES implies that immigrants have been informed of the scope and structure of the programs they agreed to participate in. Both third-country nationals and EU nationals are more likely to commit to full enrollment if they feel that a PES can offer well-performing programs that cater to their needs. The performance of the programs, as discussed above, could be a function of government funding. This study acknowledges that the motivation behind the decision to enrol could vary from financial to political. However, the perceived quality and direction of ALMPs is likely to significantly 122 contribute to jobseekers’ decision to participate. Finally, this section will explore the link between policy exposure and labour market outcomes by showing mainstreamed policies at work. As discussed in Chapter 2, both dimensions of mainstreaming, indirect and direct, can contribute to the relationship although the role of direct programs is relatively small. Indirect mainstreaming addresses employment-related concerns shared by immigrants and EU nationals such as the lack of education, skills or work experience. Direct mainstreaming is targeted at individuals with group-specific concerns such as immigrants with poor host country language skills. I will use the Danish ‘Guidance and Upskilling Program’ as an example of a mainstreamed policy (the short description is provided in Table A10) that has both ‘direct’ and ‘indirect’ components. The goal of the ‘Guidance and Upskilling Program’ is to provide unemployed individuals with clear paths to the educational services they need to obtain employment. Access to the program is conditional on formal registration with PES. Beneficiaries are unemployed individuals including recipients of sickness benefits and immigrants over the age of 18 with a legal right to stay in Denmark. Under ‘Guidance and Upskilling Program’, PES provides funding (money transfers to individuals and service providers) for two types of activities: 1) participation in general education courses 2) participation in group-specific training programs including Danish language classes for immigrants. The program represents a combination of four policy interventions. The first one, ‘Education and Training’, includes courses in Danish vocational schools and colleges for those jobseekers who are not eligible for the State Educational Support. The second and the third interventions (‘Specially Arranged Activation’ and ‘Short Guidance and Skills Qualification 123 Program’) include information meetings, on-the-job training at enterprises, assistance with interview preparation, job tests. Finally, the fourth policy intervention is the special activation measure that includes Danish language courses for immigrants. The content of the course is determined by the contract between the immigrant and the local government (municipality). Duration of the ‘Guidance and Upskilling Program’ varies from 6 weeks to 30 weeks, depending on the length of the course. To better meet the needs of jobseekers, PES cooperates with a variety of ‘social partners’ such as Danish educational institutions and enterprises. ‘Guidance and Upskilling Program’ addresses the vulnerabilities shared by both immigrants and EU nationals such as the lack of skills, vocational training, and work experience by offering measures that fall under the ‘indirect’ mainstreaming category. At the same time, the program incorporates ‘direct’ measures such as the Danish language courses for immigrants. ‘Guidance and Upskilling Program’ provides an example of how ‘direct’ policy is mainstreamed: Danish language courses fall under the same category as other ‘special arranged activation’ measures such as information meetings and on-the-job training. Such policy approach suggests that ‘direct’ policy components are viewed as important but also ranked on a par with other group-specific challenges. All types of measures can be used in any combination, depending on the needs of a jobseeker. For example, a person can enrol in a welding course in a vocational school and take part in a ‘specially arranged activation’ program such as on-the-job training in an enterprise. Or, they can take a course in Danish language in combination with an an apprenticeship program. Such flexibility allows for a personalized approach to each jobseeker as opposed to more general, one-size-fits-all methods. Another example of a mainstreamed policy at work is more typical because it only 124 includes ‘indirect’ measures (as mentioned before, ‘direct’ measures are a relatively rare policy intervention in mainstreaming). I will focus on the French “A Job for The Future” program which falls under the category of ‘Employment Incentives’ (the short description is provided in Table A11). The goal of the program is to promote the labour market integration of unemployed young people with a low level of education and/or living in disadvantaged rural and urban areas. Beneficiaries are young people aged 16 – 25 who only completed secondary education (BEP-CAP). This is a French version of state-subsidized employment: private companies receive a wage subsidy from the state if they hire a young jobseeker for a temporary or an open-contract job of 35 hours per week. The subsidy can be paid during the period of 1 – 3 years. The subsidy corresponds to 35% of the gross minimum wage. In addition to this, young jobseekers receive mobility support offered by Pôle Emploi (the French PES). “A Job for The Future” program is an example of ‘indirect’ mainstreaming since it focuses on alleviating the disadvantages shared by all young uneducated jobseekers from poor neighbourhoods. It does so by incentivizing (‘activating’) private-sector businesses, not by improving jobseekers’ human capital. Since many young immigrants live in poor deprived suburbs, they become policy beneficiaries, as well as native French citizens. The question is, whether downplaying ethno-cultural characteristics and immigrant-specific concerns in favour of socio-economic attributes strengthens or weakens the policy impact. This is a major puzzle behind mainstreaming. This section offered an insight into how all the parts of policy impact mechanism work together in practice. It explains, in a step-by-step analysis, why we expect mainstreaming to have an impact on labour market outcomes. If the causal mechanism works, we expect to see the overall increase in labour market outcomes for third-country nationals and the absence of 125 disadvantage relative to the results displayed by EU nationals (i.e.’equality’). Summary of Findings The case study analysis yields several interesting findings. First, it shows us that mainstreaming tends to interact with and support dominant patterns in ‘neighbouring’ policy domains. Second, it shows us that immigrant demography including immigrants’ employment rate in a host country is a powerful factor that can correlate with both the scope and access of mainstreaming. Third, the activation of mainstreaming in Denmark and France is shaped by the politicization of immigrant integration which has turned it into a partisan issue. In all the three cases we observe what in the ‘varieties of capitalism’ literature is called “institutional complementarity”: “two institutions can be complementary if the presence of one increases the returns from the other” (Hall & Soskice, 2001). In other words, this is the idea that “certain institutional forms, when jointly present, reinforce each other” (Amable, 2016). Countries develop mainstreaming practices that are complementary to two other frameworks: employment protection or social security policy (which is a part of welfare regime) – or both. The policy orientation in mainstreaming such as preference for certain type of programs and their functional performance are affected by the functioning of the two other institutions. One of the main findings here is that mainstreaming in labour market support helps increase returns from the two other labour market institutions. In France, the most developed and better funded programs in labour market support have focused on offering employment incentives, such as direct job creation subsidized by the state. By creating these job schemes, the state increased the number of temporary short-term contracts. Such contracts benefit French employers as they reduce the risks of being ‘punished’ by the 126 state, i.e. being involved in a bureaucratic battle against the Ministry of Labour and trade unions over dismissing a worker. Short-term contracts are safer because they involve less commitment on the employer’s part. The ‘punishment’ by the state, in turn, occurs under the strong employment protection regulation of labour market. Prioritizing direct job creation in labour market support helps increase returns from the strict employment protection regime for employers. In Denmark, policy makers have focused on active labour market policies that incentivize jobseekers. ALMPs are intended to prevent what is perceived as abuse of the generous Nordic welfare system by balancing welfare and workfare. By making unemployment benefits conditional on immigrants’ participation in ‘active’ employment support programs, the government increases insiders’ (i.e., Danish nationals) returns from the welfare regime. Another example of institutional complementarity is the focus on training and apprenticeship programs in Denmark: the activation strategies that enhance immigrants’ human capital complement the system of low employment protection in which scoring high in human capital ensures greater labour market mobility (the ability to switch between employers) and better employment outcomes. In Slovakia, ‘passive’ policy measures in employment support such as allowances and financial assistance support the rigid labour mobility system stemming from segmented housing market. In all these cases, employment support complements rather than challenges the existing policy frameworks. The contextual role of immigrant demography is as important as the role of institutional complementarity. The study shows that labour market integration is less of a problem in Slovakia as compared to Denmark and France. Bigger immigrant-native gaps are correlated with more 127 investment in employment support. Slovakia, due to its unusual demographic situation (a small number of immigrants with a high employment rate), is understandably less committed to mainstreaming in employment support and immigrant integration in general. However, it would be precarious to establish a correlation between the size of the unemployment gap and the turn towards mainstreaming. Policy responses to immigrant unemployment can vary from providing welfare benefits to targeted measures, depending on the type of institutional framework prevalent in a specific country. Both Denmark and France attempted to address the problem of immigrant unemployment with targeted measures first. Instead, the turn from targeting to mainstreaming strongly correlates with the politicization of immigrant integration. The decision to mainstream immigrant integration in the two countries that practice it, Denmark and France, follows a similar pattern: targeting measures such as the creation of the Ministry responsible for immigrant integration had become a signature policy of ‘centre-right’ governments followed by the ‘un-targeting’ and the ‘decentralization’ of integration policies by the left-leaning governments. Excessive targeting was a part of the overall ‘restrictive’ policy package. In both cases, targeting immigrants as a presumably problematic group that needs ‘special treatment’ at the governance level has become a part of the centre-right rhetoric which received a backlash from the opposite side of political spectrum as a divisive, xenophobic approach. Targeting versus mainstreaming, however, should not be seen as fluctuations on the liberalization scale. As we can see from the Denmark case, mainstreaming is compatible with restrictive policies such as integration contracts. In France, mainstreaming does not challenge the overall approach to integration based on Republican ideas of equality. The case studies show that mainstreaming has not been a radical policy shift that challenges the pillars of the long-standing 128 approach to integration. Instead, it should be seen as a new policy tool designed to achieve the same integration goal within the same integration framework. In France, mainstreaming has evolved as a ramification of the ‘colour-blind’ policy attitude. In Denmark, it has become a new strategy compatible with the prevalent restrictive approach. The case studies lay a background for the analysis of the causal mechanism of policy impact. By looking at how mainstreamed policies work in Denmark and France we see that despite cross-country differences in policy patterns mentioned in the case studies (Danish policies ‘activate’ jobseekers by prioritizing training programs and France focuses on incentivizing employers), the overall trajectory of policy impact remains the same: both countries are committed to investing in ALMPs and use ‘indirect’ and ‘direct’ mainstreaming to make employment support policy more responsive to jobseekers’ needs. Whether this strategy is likely to lead to better employment outcomes is discussed in the next chapter. 129 Chapter 6. Policy Impact: Quantitative Analysis This chapter seeks to evaluate the impact of policy mainstreaming in employment support on the labour market integration of third-country nationals in the EU. First, it examines the level of exposure of TCNs to labour market support policies, both in absolute terms and relative to EU nationals. Furthermore, this chapter evaluates the link between policy exposure and labour market outcomes for all groups of jobseekers. I will start by restating the research question and hypotheses and discussing estimation strategies used for hypothesis testing. I will then proceed with presenting the data, samples, and measures. This part is followed by a stepwise model analysis. The rest of the chapter discusses key findings from the models. Research Question and Hypotheses The purpose of my analysis is to examine the impact of mainstreamed policies on labour market integration. Does mainstreaming contribute to a change in labour market outcomes for third-country nationals? The answer to this overarching research question is based on two components: the extent of policy exposure, and the relationship between policy exposure, and labour market outcomes. As previously discussed, the first component, the level of policy exposure jobseekers receive, may involve simply contacting PES to find out about job vacancies and putting one’s name on the employment office list to be contacted later ‘if something comes up’. At a more substantive level, policy exposure may also involve direct engagement in labour market support programs such as training and apprenticeship courses. Participation in some programs is only available to jobseekers who officially register with PES. Unlike contacting PES, registration 130 implies a commitment of time and effort. I measure the level of policy exposure, both ‘contacting PES’ and ‘registration with PES’, for TCNs and EU nationals. Most importantly, I examine whether the impact of resident status on policy exposure is higher in countries with more robust levels of mainstreaming. The second component is the link between policy exposure and labour market outcomes. I examine, whether higher levels of engagement in labour market support programs are associated with better employment outcomes among third-country nationals and EU nationals. I also look at the proportion of TCNs who have found employment due to employment assistance. Finally, I examine whether the relationship is more pronounced in EU countries that score higher in mainstreaming. I measure the impact of mainstreaming both in absolute terms, i.e. by examining its overall effect on employment outcomes, and in relative terms, by looking at which group of EU residents gets more policy exposure and benefits more from employment assistance. The latter finding is valuable because the purpose of mainstreaming is to increase equality between majority groups and disadvantaged minorities, i.e. TCNs, at the policy level. A higher level of similarity in policy exposure and labour market outcomes is likely to indicate a higher level of equality. If mainstreaming works, TCNs are expected to have the same or higher level of cooperation with PES as EU nationals and benefit from enrolment in employment support programs. At the country level, I look at whether countries with higher levels of mainstreaming display a higher level of equality between EU nationals and TCNs with regard to policy exposure and labour market outcomes. Based on the above, my study tests the following single-level hypotheses: H1: Third-country nationals in the EU have the same or higher level of policy exposure 131 to labour market support as EU nationals. H2: Third-country nationals in the EU find employment through PES as often as or more often than EU nationals. H3: Higher policy exposure of TCNs to mainstreamed labour market support is correlated with better job market performance. At the country level, policy variation in labour market support is expected to affect the relationships specified in H 1 – 3. I expect that the individual-level relationships between resident status and policy exposure become more robust in EU countries with higher levels of employment policy support (H4). Group-level policy variation is also expected to strengthen the relationship between resident status and labour market outcomes (H5). Finally, I expect that in the EU countries that invest more in employment support policies, policy exposure leads to better employment outcomes for TCNs (H6). H4: The effect of immigrant status on policy exposure is stronger in countries that score higher in mainstreaming in labour market support. H5: The effect of finding employment through PES is stronger for third-country nationals as compared to EU nationals in countries that score higher in mainstreaming in labour market support. H6: The effect of policy exposure on TCNs’ labour market outcomes is stronger in countries that score higher in mainstreaming in labour market support. Estimation Strategies This study uses three estimation methods: (1) cross-tabulation/contingency table analysis, (2) fixed effects single-level binomial logistic regression analysis, and (3) Bayesian random- 132 effects multilevel logistic regression analysis. As explained in Chapter 3, the use of both fixed effects single-level and multilevel models is a trade-off between the advantages and disadvantages of each method. As discussed in Chapter 3, the method choice is determined by the overall strategy of cross-validation. Under the cross-validation approach, the same type of relationship (between resident status and policy exposure) is estimated by two methods, cross-tabulation analysis and regression analysis. One method verifies the results obtained from the other. If there is a clear pattern, we should expect to observe consistency in outcomes. Second, at both levels, single-level and multilevel, the relationship between policy exposure and labour market outcomes is measured by various models. Again, we can report a pattern if the results are consistent across the models. The specifics of the hypotheses and data characteristics are discussed in subsequent sections that guide us through the stepwise analysis strategy (summarized in Table 8). Cross-Tabulation and Single-Level Regression Individual-level data are drawn from the European Union Labour Force Survey (EU-LFS). Most variables in the Labour Force Survey are categorical and the key variables selected for analysis including the dependent variables are binary, which allows for cross-tabulation analysis and logistic regression analysis. Both single-level regression analysis and cross-tabulation tables are used to estimate the averages across EU countries. Cross-tabulation is used to estimate the individual-level relationships between resident status in the EU and ‘policy exposure’ measured as (1) contacting PES to find work; and (2) being officially registered with PES, as indicated in H1. Cross-tabulation is also used to examine the relationship between 133 resident status and labour market outcomes, measured as finding a job as a result of involvement in labour market support programs (H2 and H3). In the fixed effects single-level models, I estimate (1) the average impact of resident status on policy exposure and labour market outcomes across all EU countries in the sample and (2) the average impact of policy exposure on labour market outcomes for TCNs, without accounting for country-specific policy distinctions. Individual-level logistic regression is used to estimate which group, TCNs or EU nationals, is more likely (1) to contact PES; (2) to be officially registered as a jobseeker at PES; (3) to benefit from labour market support policies provided by PES. Single-level regression is also used to examine; (4) if there is any correlation between policy exposure and labour market outcomes across EU countries for TCNs. Checking for Between-Group Variability After performing single-level regression analysis, we need to decide if we should proceed to the next step of multilevel analysis. The answer is affirmative since the study involves a multilevel data structure: individuals (TCNs) are ‘nested’ within groups (countries). The hypothesized relations between the dependent variable and individual-level predictors operate across countries with different levels of policy mainstreaming in employment support. However, it is also important that we learn how much variability there is between groups (countries). If it is zero or close to zero, the observations within groups are no more similar than the observations from different groups with the result that running a multilevel model with random effects is not necessary. If a between-group variation is higher than zero it is meaningful to understand how much of the overall variation in the outcome is captured by group-specific distinctions. 134 Table 8 Hypotheses, Estimation Strategies, and Models Inference of interest Hypothesis Estimation strategy Model Policy exposure H1 1. Cross-tabulation analysis 2. Single-level logistic regression analysis Model 1: ‘Policy exposure’ measured as ‘contacting PES’ Model 2: ‘Policy exposure’ measured as ‘registration at PES’ The link between policy exposure and labour market outcomes H2 1. Cross-tabulation analysis 2. Single-level logistic regression analysis Model 3. ‘Policy effect’ measured as the odds of finding a current job via PES H3 1. Cross-tabulation analysis 2. Single-level logistic regression analysis Model 4. ‘Policy effect’ measured as the link between registration at PES and labour market outcomes The impact of mainstreaming on policy exposure H4 Multilevel models with (1) random intercept (2) random coefficient (3) interaction term. Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 ‘Policy exposure’ measured as ‘contacting PES’ ‘Policy exposure’ measured as ‘registration at PES’ The impact of mainstreaming on the relationship between policy exposure and labour market outcomes H5 Multilevel models with (1) random intercept (2) random coefficient (3) interaction term. Model 10 Model 11 Model 12 ‘Policy effect’ measured as the odds of finding a current job via PES H6 Multilevel models with (1) random intercept (2) random coefficient (3) interaction term. Model 13 Model 14 Model 15 ‘Policy effect’ measured as the link between registration at PES and labour market outcomes 135 My first point of interest is therefore the random intercept variance for countries, Var (uoj). It indicates how much groups differ from each other; for example, in the model with ‘contacting PES’ as a dependent variable and ‘resident status’ as a predictor, the random intercept variance is the deviation of the specific log-odds of contacting PES in a given country from the overall log-odds of contacting PES across all the countries. The higher the random intercept variance, the larger the variation of the log-odds of contacting PES from one country to another. Simply put, a higher random intercept variance indicates that country-level variation matters and that running a multilevel model is warranted. To obtain the random intercept variance, I run an empty model without predictors. In the following equation, 𝛽00 is the fixed intercept – the overall log-odds of a given dependent variable across all countries; eij is the individual-level error term associated with person i; u0i is the error term associated with country j. Yij = 𝛽00 + eij +u0j To further quantify the degree of homogeneity between groups, this study uses the ICC – the intra-class correlation coefficient. It estimates the proportion of group-level variance compared to the total variance. ICC = ,-. (0𝑜𝑗),-. 0𝑜𝑗 % 𝜋 ^6/8 It ranges from 0 to 1; an ICC that is close to 1 indicates that observations depend on group membership (there is a high similarity between values of the same group); an ICC that is close to zero indicates that values from the same group are not similar. A high random intercept variance indicates a high ICC. It should be noted, however, that the ICC is also dependent on the 136 number of groups; since the number of countries in the sample is small, the likelihood of obtaining a high ICC in the model is pretty low. Bayesian Multilevel Regression If the intra-class correlation suggests that there is substantial between-group variability I proceed with multilevel regression. I use a set of Bayesian random effects models to examine whether the group-level variation between countries with different levels of policy mainstreaming affects the relationships between resident status and policy exposure/labour market outcomes on one hand, and the relationship between policy exposure and labour market outcomes for TCNs on the other. As discussed in Chapter 3, the choice of a Bayesian framework as opposed to employing maximum likelihood modelling is based on the relatively small number of groups in the sample. The models summarize the posterior probability distribution of all model parameters, their standard errors, and credible intervals based on the observed data. Posterior distributions are estimated via MCMC (Markov Chain Monte Carlo) sampling without any large sampling approximation. Each multilevel set includes three models: (1) a random intercept model, (2) a random coefficient model, and (3) a model with cross-level interactions between the individual- and country-level predictors. In the following equations 𝛽00 is the general constant term; 𝛽 10 is the effect of the individual-level variable Xij across all countries; 𝛽 01 is the effect of the group-level variable Xj , eij is the individual-level error term associated with person i; u0i is the error term associated with country j; u1j is the random coefficient variance – the effect of the individual- 137 level predictor within a specific country; and 𝛽11 is the coefficient parameter20 associated with the cross-level interaction. Random intercept model: Yij = 𝛽00 + 𝛽10 (Xij) + 𝛽01 (Xj) …+ eij +u0j Random coefficient model: Yij = 𝛽00 + ( 𝛽10 + u1j )Xij + 𝛽01 (Xj) …+ eij +u0j Cross-level interaction model: Yij = 𝛽00 + ( 𝛽10 + u1j )Xij + 𝛽01 (Xj) + 𝛽11 Xij Xj …+ eij +u0j I use the DIC (Deviance Information Criterion), a Bayesian method of model comparison, to select a model with the lowest values of DIC in each model set. The smallest DIC estimates the model with the best fit, i.e. the model that will make the best short-term predictions. The models with the highest DIC (5 –point difference or higher) are ruled out as misfit and only the results from the best fit models are reported. If adding an interaction term improves the fit of the model, this provides the strongest level of support for the hypotheses since this indicates that labour market integration is conditional on the interaction between a country-level integration policy variable and an individual-level predictor. In other words, it suggests that a specific integration policy matters. Three other important criteria used for hypotheses testing are: (1) the direction of the relationship between the key predictor(s) and the outcome variable measured by the posterior parameter (e.g. 20 As mentioned in Chapter 3, in Bayesian framework, this is a parameter, not an estimate. 138 the odds of being employed for TCNs in the EU as compared to EU nationals and whether the probability of being employed is a function of integration policies); (2) the posterior variance which characterizes the dispersion of the parameter; and (3) the 95% credible interval for each parameter that gives the posterior probability that a parameter (coefficient) lies in this range. I use the highest density intervals (HPD) with the smallest range. Each multilevel model consists of two parts, a fixed effects part and a random21 effects part. Fixed quantities provide averages across all EU countries as in single-level regression. For example, the average impact of being registered at PES on the odds of being employed via PES across the EU is a ‘fixed’ slope that does not account for country-specific distinctions. The random effects part reflects the fact that the data are clustered. Each random intercept model has the same number of individual-level and country-level controls, but the key individual-level predictor is different depending on which hypothesis is being tested. All continuous predictors are grand mean centred. A few words should be said about the interpretation of interaction effect. As Sommet and Morselli (2017) tell us, in multilevel logistic regression, the coefficient of the product term is not mathematically equal to the interaction effect. Statistical software calculates the product term coefficient the same way it does for the main effect (e.g. the impact of mainstreaming on policy exposure). In logistic regression, the sign and the value of the interaction effect might be biased. There has been a scholarly debate on how to address the bias. This study pursues the standard significance-of-the-product approach which in Bayesian framework means ‘the credibility-of-the interval’ approach. Under this approach, one only looks at the credibility of the interval associated with the interaction term, without further analysis. 21 ‘Random’ means that the slope is allowed to vary across clusters. 139 Diagnostics The multilevel models are estimated using Markov chain Monte Carlo (MCMC) sampling. For each model, the burn-in length is 2, 000 iterations and a monitoring chain length is 10, 000 iterations. All regression coefficients are assigned default normal priors with zero means and variances of 10,000. Data and Samples The EU Labour Force Survey is the largest European household sample survey and the main source for European statistics about the situation and trends in the EU labour market; e.g. it forms the basis for the EU’s monthly harmonized unemployment rate (EU Labour Force Survey, 2021). Respondents aged 15 and above were selected from the resident population from 35 participating countries including the 28 Member States of the European Union, three EFTA countries (Iceland, Norway and Switzerland), and four EU candidate countries (Montenegro, North Macedonia, Serbia and Turkey). The EU-LFS provides quarterly and annual data from 2005 – 2018. I use the anonymized EU-LFS Microdata – sets of records that contain confidential information on individual persons and households22. The structure of the data does not allow for panel data analysis. Although most countries have a rotation scheme in place, the member states decided not to allow the tracking of persons across waves for the purpose of data protection; the household numbers are randomized per dataset. I will therefore treat the EU-LFS as cross-sectional data. 22 Access to the EU-LFS Microdata set was granted by the Eurostat on September 24, 2019. 140 My sample includes the latest available individual-level data from the 2018 wave of the EU-LFS. The sample for individual-level analysis is restricted to 17 European countries: Austria, Belgium, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Poland, Portugal, Spain, Sweden, Switzerland, and the United Kingdom. All these countries provide access to public employment services to four major categories of TCNs (as indicated in the Index of Policy Mainstreaming in Labour Market Support). Under limited access, the unbiased comparison between EU nationals and TCNs cannot be performed since TCNs will obviously have a disadvantage; therefore, countries that limit access to PES for certain categories of TCNs have been excluded from the analysis. Policy variation, therefore, refers only to the policy scope, not to access to policies. The number of observations in the sample is 704,453. The total number of third-country nationals in the sample is 27,612; the number of EU nationals is 677,220. For multilevel regression analysis, the individual-level information from the EU-LFS was matched with the latest available country-level policy data from the Index of Mainstreaming in Labour Market Support (2016), the latest available data from CIVIX (2014), OECD (2018) and Eurostat (2017, 2018). For two countries, the UK and Switzerland, policy data are not available, so the number of countries for multilevel analysis has been reduced to 15 EU/EFTA states: Austria, Belgium, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Poland, Portugal, Spain, and Sweden. 141 Dependent and Independent Variables Policy exposure is measured by two separate survey items, “contacted public employment office to find work” and “registered at public employment office”, both recoded to range from 0 (‘No’) to 1 (‘Yes’). ‘Contacting PES’ is used as both a dependent and an independent variable. It is a dependent variable in the single-level regression model/contingency table that estimates the effects of the EU resident status on policy exposure and an independent variable in the models that estimate the effects of policy exposure on the expected outcome (finding employment). According to the EU-LFS Codebook, "the 'contact with the public employment office to find work' only involves: - putting the respondent's names in the employment office files for the first time (after a spell of employment or inactivity); - finding out about possible job vacancies, or - 'at the initiative of the employment office a suggestion of a job opportunity', which may be accepted or refused by the job searcher.” (European Commission 2018, p. 93). ‘Contacting PES’ should be distinguished from ‘being registered at public employment office’; the latter implies official registration as a jobseeker and having access to the services available to registered jobseekers. Policy exposure is therefore operationalized at two levels: as a job strategy that does not require a commitment and as more direct involvement in employment support programs. The ‘registered at PES’ survey item is used (1) as a binary variable and (2) in its original disaggregated form, as a four-value variable that has been recoded to run from 0 (the least amount of state-provided assistance) to 4 (the highest level of state-provided assistance). The variable has the following categories: “persons who are not registered at PES and do not receive 142 benefit or assistance” (0) “persons who are not registered at PES but receive benefit or assistance” (1), “persons who are registered at PES but do not receive benefit or assistance” (2), and “persons who are registered at PES and receive benefit or assistance” (3). ‘Contacting PES’ addresses those EU-LFS respondents who are seeking employment during the reference week. ‘Being registered at PES’ covers everybody aged 15 – 74 who participated in the survey, including individuals who are not seeking employment (e.g. an employed respondent who wants to switch jobs). Labour market outcome is used as a dependent variable only. It is measured by the survey item “involvement of the public employment office at any moment in finding the present job” with the values 0 for ‘No’ and 1 for ‘Yes’. The question only addresses respondents who consider themselves employed during the reference week. As indicated in the EU-LFS Codebook, “the involvement of the public employment office should be effective, i.e. having contributed to the finding of the current job. It should be a role of putting in contact the employer and the employee by informing one of the existence of the other” (European Commission 2018, p. 44). Training courses, or other activities aimed to improve the skills of jobseekers, do not imply “putting the person in contact with the current employer”, are not considered as contributing to the finding of the current job. EU resident status is only used as an independent variable. It has been recoded to a categorical variable with the value 0 for the EU/EFTA nationals and 1 – for immigrants. Immigrant status in my study is restricted to the TCN status. For a more nuanced analysis, the same variable has been recoded into a categorical variable with three groups: country natives, intra-EU migrants, and TCNs. According to the OECD Indicators for Immigrant Integration (2015), “TCNs” in the 143 context of the European Union are understood to be non-EU, or third-country nationals who reside legally in the European Union.” (p. 300). According to the definition provided by the Directorate-General for Migration and Home Affairs of the European Commission, a third country national (TCN) is “any person who is not a citizen of the European Union within the meaning of Art 20 (1) of TFEU and who is not a person enjoying the European Union right to free movement, as defined in Art. 2 (5) of the Regulation (EU) 2016/399 (Schengen Borders Code).” The EU right to free movement applies to the member states of the European Economic Area (EEA) which includes the EU, four states outside the EU (Norway, Iceland, Liechtenstein, and Switzerland). In accordance with the above, TCN status is measured with a single EU-LFS item measuring citizenship (‘Nationality’). The survey item is recoded to a binary variable with the value 0 for respondents who are citizens of the EU-28 and EFTA (Norway, Iceland, Liechtenstein and Switzerland) and 1 for everyone else (i.e. third-country nationals). Due to data limitations, our study does not distinguish between different types of TCNs. Mainstreaming in labour market support is a country-level independent variable used in the multilevel models. The policy data are drawn from the Index of Mainstreaming in Labour Market Support (the measurements are discussed in Chapter 4). Individual-Level Control Variables All individual-level control variables are drawn from the EU-LFS. All of them were recoded. Gender is a dummy variable with the value 0 for women and 1 for men. Age is a derived variable that is delivered in 5-year age bands (e.g. 2 for 0-4, 7 for 5-9 etc) since the original (‘core’) data on the age of respondents is not included in the anonymized EU-LFS microdata for the sake 144 of data protection. Education is aggregated to three levels: ‘low’ (lower secondary), ‘medium’ (upper secondary), and ‘high’ (tertiary/higher education). Country-Level Control Variables Multilevel models contain several country-level control variables (see Table A41). Some (for example, the GDP rate) can correlate with both the resident status in the EU and the level of employment among EU and non-EU citizens, which is why leaving them out might result in biased estimates. The overall unemployment rate per country is a continuous variable obtained from OECD data (2020). It is the number of unemployed people as a percentage of the labour force; the labour force consisting of the unemployed or those in paid or self-employment. GDP per capita variable is also drawn from the OECD data (2020b). GDP is measured in US dollars at current prices and PPPs (Purchasing Power Parity rates). The foreign population in the EU is data drawn from the Eurostat and OECD (‘Stocks of foreign population by nationality in OECD countries and in Russia’). It is the size of immigrant populations as measured in numbers of persons (OECD, 2020b). Finally, I control for variation in welfare regimes across Europe. It is measured as expenditure on social protection per inhabitant: social benefits, administration costs, miscellaneous expenditure by social protection schemes. The data on welfare are drawn from Eurostat (2022). 145 Unobserved Variation This study acknowledges that there might be potential confounding variables that have not been included in the analysis due to data limitations. The attributes of these variables may affect the outcomes across the models. First, at the individual level, this study does not account for the minority status of third-country nationals, e.g. their race or religion. Not all TCNs belong to visible minority groups. Since discrimination against immigrant minorities affects their hiring and treatment at work, minority status can be a potential confounder in terms of policy effect, i.e. it can lower the chances of finding employment via PES. As explained in Chapter 2, more than half of TCNs in the sample are from non-white majority regions and the portion of North American immigrants is negligible. Although one cannot control for minority status, we can assume that most respondents in the sample are vulnerable to racism in the labour market. Second, due to data limitations, the study does not account for TCNs’ level of work experience in the host country which, again, can be an important factor affecting employment outcomes. The individual-level confounders such as race and work experience have more potential to interfere with labour market outcomes than with exposure to labour market support. However, individual-level confounding is more likely to affect labour market outcomes than policy exposure. PES, whose responsibility is to support disadvantaged groups (including minorities) are less likely to discriminate against such groups than host-country employers. Immigrants’ work experience is unlikely to affect the chances of contacting PES or registration at PES. The country-level unobserved variation includes but is not limited to the strength of anti-discrimination legislation in the host country which can negatively affect the labour market performance of immigrant jobseekers despite their involvement in PES programs. However, 146 despite gaps in specific areas, in all EU countries people have basic legal protection against ethnic, racial, religious, and nationality-based discrimination in all areas of life (MIPEX 2020). Furthermore, at the country level, this study does not account for local policy variations of mainstreaming. It only takes into account national policies. In some countries such as Denmark, PES have power to develop programs that only work locally, at the provincial or municipal level. The labour market performance of immigrants can therefore be affected not only by the overall strength of mainstreamed programs but also by policy variation across regions within countries. The problem of unobserved variation suggests further need to expand the scope of the current analysis by including additional predictors in the analysis. Additionally, as discussed previously in Chapter 3, the problem of cluster confounding at the country level is addressed by using single-level models. Cross-Tabulation and Regression Analysis Intra-EU Migrants and Country Nationals As mentioned in Chapter 2, although the main inference of interest is the impact of mainstreaming on the labour market outcomes of third-country nationals, this study seeks to provide a more nuanced approach by looking at other types of immigrants. I distinguish between EU nationals who look for employment in their own country (‘country nationals’) and intra-EU migrants. To examine whether there is any difference in outcomes between country nationals and intra-EU migrants, I conduct a single-level regression analysis, a cross-tabulation analysis, and a multilevel regression analysis with all the three groups. The purpose of the analysis is to see if 147 the similarities between country nationals and EU migrants are big enough to use all EU nationals as a reference group. The findings from the single-level regression analysis and contingency tables cross-validate the following pattern: there is little difference between country nationals and intra-EU migrants in reference to policy exposure. Among those jobseekers who contacted PES, the number of unemployed EU nationals looking for a job in their own country and of intra-EU nationals is almost the same: 36% and 34%, respectively (see Table A13). We observe a similar pattern in the regression analysis: the odds of contacting PES are only 9% lower for EU migrants as compared to country natives (Table A14). Figure 18 shows that there is no difference between country natives and intra-EU migrants with regard to registering with PES: the predicted probability of getting registered is 0.078 for both groups, as compared to 1.113 for TCNs. In the regression analysis, the odds ratio is equal to 1, which indicates no difference (Table A15, Table A16). As for policy effects, intra-EU nationals tend to benefit from involvement in PES slightly less than country nationals. However, the averages across all the groups are minimal: Figure 19 shows that predicted probabilities are 0.06 for country nationals, 0.05 for intra-EU migrants and 0.07 for TCNs. On average, both EU nationals and country nationals received their present job using job search methods other than public employment services: 94% and 95%, respectively (Table A17). I use ML multilevel analysis to verify the outcomes for intra-EU migrants. As discussed in Chapter 3, in samples with a big number of individuals, both Bayesian and MLE models can produce unbiased estimates for individual-level predictors even if the number of groups is small. I therefore use MLE to verify the impact of resident status, an individual-level predictor. 148 Figure 18 Predicted Probability of Registering with PES for Three Categories of EU Residents Figure 19 Predicted Probability of Getting Employment via Involvement in PES Programs for Three Categories of EU Residents Note: See Table A16 for results of regression analysis. Note: See Table A18 for results of regression analysis. Findings on intra-EU migrants obtained from multilevel analysis are summarized in Tables A34 - A38. They are not statistically significant except for the model that reveals that the odds of registering with PES are higher for intra-EU migrants as compared to country natives (Table A35). This finding, however, is not cross-validated by single-level models that measure the same type of relationship. Based on the results of the analysis, I use all EU nationals as a reference category. Policy Exposure: EU Nationals vs TCNs The findings from the cross-tabulation analysis (Table A22) suggest that 36% of unemployed EU nationals contacted a public employment office to find a job, while a slightly higher percentage of unemployed third-country nationals (43%) did so. Both groups of respondents stated that they were seeking employment during the reference week. Most 149 jobseekers, however, did not use public employment services as a job search strategy (64% of unemployed EU nationals and 57% of unemployed TCNs). The logistic regression analysis conducted with the same independent and dependent variables validates this finding. The results of the analysis are summarized in Table A23 and Figure 20. There is evidence that the odds of contacting PES are higher if a jobseeker is a third-country national. Holding other predictors constant, the odds of contacting PES increase by about 30% if a person is an immigrant. Due to a small p-value (0.000), this result is statistically significant. Figure 20 shows that the predicted probability of contacting PES is higher (0.42) for TCNs than for EU nationals (0.36). Figure 20 Predicted Probability of Contacting PES for EU Nationals and Third-Country Nationals Note: See Table A23 for results of regression analysis. 150 All the control variables (age, gender, education) are found to be significantly related to contacting PES. Older individuals are 1.007 times more likely to contact PES than younger jobseekers (although 0.7% increase is not a meaningful difference); men and women have the same odds of contacting PES. Importantly, there is a negative relationship between education and policy exposure: for more educated jobseekers the odds of contacting PES decrease by about 12%. In the next step I examine how many respondents in each group, both employed and unemployed, were registered at a PES office during the reference week. For cross-tabulation analysis, I use a dependent variable ‘registration at PES’. For logistic analysis, the same variable has been recoded into a binary variable: ‘registered’ vs ‘not registered’. The cross-tabulation analysis (see Table A24) suggests that, among TCNs, the total proportion of individuals registered at PES is twice as high (15.3%) as the number of EU nationals registered at PES (8%). There is a 7% gap between the number of EU nationals not registered at PES (92%) and the total number of third-country nationals who do not receive welfare payments and are not registered with PES (85%). The logistic analysis of a relationship between the same two variables (see Table A25) indicates that the odds of being registered as a jobseeker increase by 80% if a person is an immigrant, holding other predictors constant; third-country nationals are 2 times more likely to register with a public employment service than EU nationals (1.8 ÷ 1). The p-value lower than 0.01 indicates that in fewer than 1 in 1, 000 samples would we observe a difference of this size if the null hypothesis were true, hence we can reject the null hypothesis. Figure 21 shows that the predicted probability of getting registered is higher (0.13) for TCNs than for EU nationals (0.08). Age, gender and education are also significantly related to the dependent variable. Men are 1.2 151 times less likely to register with a national employment service than women; with the odds ratio equal to 1, age does not make a difference and finally, more educated respondents are 1.4 times less likely to register as jobseekers. Figure 21 Probability of Registering with PES for EU Nationals and Third-Country Nationals Note: See Table A25 for results of regression analysis. Both contingency tables and logistic regression analysis point at the same pattern – TCNs have a higher level of exposure to labour market support policies than EU nationals. As compared to the EU nationals, there is a higher number of TCNs who choose to register as jobseekers and contact the national employment office to find work. 152 The Relationship Between Policy Exposure and Employment Outcomes: EU Nationals vs TCNs The next model examines the link between policy exposure and labour market outcomes: specifically, it addresses a question of whether involvement in PES played a role in finding current employment for different groups of respondents and if so, which group of EU residents was more likely to benefit from this involvement. I focus on two groups of employed EU residents: EU nationals and TCNs. The results from the contingency table (Table A26) suggest that the vast majority of employed EU residents received their present job using job search methods other than public employment services. There is almost no difference in this respect between EU nationals (94%) and TCNs (93%). Third-country nationals tend to benefit from employment support slightly more than EU nationals but the difference in policy impact is too small (1.31%) to be considered as meaningful. A small p-value (<0.01) validates this result. Logistic regression analysis supports this finding (Table A27). Holding other predictors constant, the odds of benefiting from labour market support policies provided by PES are slightly (1.2 times) higher for TCNs, which indicates a 17% increase but the borderline p-value (0.048) does not provide strong evidence against the null hypothesis. Figure 22 shows that the predicted probabilities of finding employment via PES are almost the same for both groups: 0.06 for EU nationals and 0.07 for immigrants. With the borderline p-value (0.05), gender is hardly a significant predictor. As with policy exposure, more educated respondents are slightly (1.2 times) less likely to report that PES played a role in finding their current job. Finally, there is a positive but weak relationship between age and labour market outcomes. 153 Figure 22 Probability of Finding Employment via Involvement in PES Programs for EU Nationals and Third-Country Nationals Note: See Table A27 for results of regression analysis. The next two models also estimate the impact of policy exposure on finding employment. two groups of EU residents, TCNs and EU nationals. Each model is restricted to a specific group of employed residents. Policy exposure is measured as ‘being registered at PES’, the dependent variable is ‘involvement of the PES in finding the present job’.23 There is a strong positive relationship between being registered as a jobseeker at PES and finding a job with assistance from PES. All else equal, the odds of finding a job via PES are 4 times higher for TCNs who are registered at a national employment office as compared to unregistered TCNs (Table A28) and 2.35 times higher for registered jobseekers with EU citizenship as compared to EU nationals who 23 ‘Contacting PES’ cannot be used as a predictor because this variable is restricted to unemployed respondents only, whereas the dependent variable is applied to employed respondents. ‘Being registered at PES’ is a survey item that is applied to everybody in the dataset. 154 are not registered (Table A29). Similarly, Figure 23 shows that the predicted probability of finding employment is much higher for registered TCNs (0.19) than for unregistered TCNs (0.06). Figure 24 shows that the difference is quite big for registered and unregistered EU nationals (0.05 and 0.12, respectively). Both TCNs and EU nationals tend to find employment using job search methods other than public employment services but among those jobseekers who are registered at PES, TCNs are more likely to find employment than EU nationals. For TCNs, the odds of getting employment with assistance from PES increase by about 288% if they register as jobseekers, as compared to simply ‘contacting PES’ (e.g. browsing their website) or using alternative job search strategies. Figure 23 Probability of Getting Employment via Registering with PES for Third-Country Nationals Figure 24 Probability of Getting Employment via Registering with PES for EU Nationals Note: See Table A28 for results of regression analysis Note: See Table A29 for results of regression analysis 155 This number with a p-value lower than 0.01 in both models, only in 10,000 samples would we observe a difference of this size if the null hypothesis were true. For EU nationals, the odds of getting a job through registration in PES programs increase by 137% if they register. In the sample with TCNs, age, gender and education do not significantly affect the role of PES in finding the present job. In the sample with EU nationals, only gender does not make a big difference (p-value = 0.05). The role of age is minimal (0.009); and more educated EU nationals are 1.08 times less likely to benefit from employment support policies. Multilevel Analysis: Country-Level Distinctions in Mainstreaming I will start the discussion of country-level predictors with summary statistics obtained from cross-tabulation analysis (Table 9). With regard to mainstreaming and policy exposure, Sweden has the highest percentage of unemployed TCNs who contacted PES to find a job (80%) and the highest percentage of TCNs who registered as jobseekers (33%). Its spending on employment support is also quite high (1.74% of GDP) but not the highest in the sample. France invests in employment support more than any other country in the sample (2.84%) and its estimates of policy exposure are high as well (60% and 28%). On the other hand, Greece and Poland have the lowest amount of spending on employment support in the EU (0.68% and 0.62%), and also the lowest levels of policy exposure. The impact of PES on employment outcomes is strongest in Sweden: 31% of the total number of employed TCNs reported that they received their current job due to assistance provided by PES. Policy effect is also pronounced in Finland, the country that falls into the upper quartile with regard to public spending on employment support (2.53% of GDP): 28% of the total number of employed TCNs states that PES played a role in their job search. 156 Table 9 Summary Statistics. Results from Country-Level Cross-Tabulation Analyses Country Policy score in mainstreaming Policy exposure: unemployed TCNs who contacted PES to find a job (% of the total number of unemployed TCNs) Policy exposure: TCNs registered with PES (% of the total number of TCNs) Policy effect: TCNs who have a job due to the PES involvement (% of the total number of employed TCNs) Austria 2.19 74.87 14.05 16.02 Belgium 2.27 62.94 23.22 6.67 Estonia 0.80 48.06 5.71 11.50 Finland 2.53 44.44 25.73 28.12 France 2.84 59.58 28.30 14.12 Germany 1.40 61.18 16.79 10.55 Greece 0.68 54.88 15.93 2.60 Ireland 1.22 35.14 no obs 2.53 Luxembourg 1.32 49.40 15.20 22.45 Netherlands 2.14 17.95 23.39 7.22 Poland 0.62 33.33 2.27 0.00 Portugal 1.43 41.51 8.92 2.80 Spain 2.19 24.26 23.68 2.26 Sweden 1.74 80.00 32.78 31.25 Switzerland N/A 34.97 9.27 7.59 United Kingdom N/A 15.07 1.64 4.74 157 Poland and Italy have a just above zero percentage of TCNs for whom the assistance provided by PES has worked. I will use multilevel analysis to examine if these summary data are an indication of any meaningful patterns across the EU. The first step is to establish whether running a multilevel model is warranted. I specify a set of ‘empty models’ for each dependent variable to see how much of the total variance is at group level. Models 4 – 6 In the empty model with no predictors (Table A30), the log-odds of contacting PES is allowed to vary from one country to another. The random intercept variance for countries is 0.55, which yields an ICC equal to 0.15 (ICC = 0.55/(0.55 + 3.29)). This indicates that 15% of the variance of contacting PES is explained by between-group differences and, conversely, – that 85% is explained by within-country differences. I find 15% to be a substantial number and therefore proceed with stepwise multilevel analysis. The best fit model in the set of multilevel models is the model with cross-level interactions that has the lowest DIC values (38109.55). The second best fit model is the random coefficient model (38110.72). The random intercept model is excluded from the analysis due to a more than 5-point difference in DIC scores (38139.54). In the multilevel model (Table 10), EU resident status continues to be a significant predictor: on average, TCNs are 1.56 times (56%) more likely than EU nationals to contact PES when they seek employment. This finding cross-validates the results from single-level analysis. 158 Table 10 Cross-Level Interaction Model, with ‘Contacting PES’ as a Dependent Variable Independent Variable Odds Ratio St. Deviation MCSE Median HPD [90% Credible Interval] EU Resident Status (TCNs) 1.56 0.56 0.11 1.56 1.53 – 1.66 Gender 1.00 0.01 0.00 1.00 1.00 – 1.03 Age 1.00 0.00 0.00 1.00 1.00 – 1.01 Education 0.83 0.01 0.00 0.83 0.82 – 0.85 Mainstreaming Policy score 2.35 0.06 0.01 2.35 2.26 – 2.46 CIVIX Score 1.02 0.01 0.00 1.02 1.00 – 1.05 Unemployment Rate 0.84 0.01 0.00 0.84 0.82 – 0.86 Foreign Population in the EU 1.20 0.02 0.00 1.28 1.18 – 1.23 GDP 0.86 0.02 0.00 0.86 0.83 – 0.89 Welfare 1.00 0.02 0.00 1.00 0.96 – 1.03 EU Resident Status x Mainstreaming Policy Score 0.96 0.02 0.00 0.96 0.92 – 1.00 Constant 0.28 0.00 0.00 0.27 0.92 – 1.00 Random Intercept Variance Var (uo). 0.20 0.12 0.01 0.17 0.06 – 0.34 Random Coefficient Variance Var (u1) 0.20 0.14 0.00 0.16 0.02 – 0.37 159 Congruent with H1, the labour market support policy score has a meaningful positive effect on policy exposure: for every point increase in mainstreaming, the odds of contacting PES for both groups of EU residents increase by 2.35 (or, by 135%). However, there is no evidence that either category of EU residents, TCNs or EU nationals, contacts PES as a result of policy variation in mainstreaming: the interaction term lies in the negative credible interval, which is why the effect does not require further interpretation. There is a 90% probability that this parameter lies in the positive credible interval between 2.26 – 2.46. This is a small interval (0.2), which adds credibility to the result. Among other country-level predictors, there is a positive relationship between the size of foreign population in a country and the odds of contacting PES (20% increase). Civic integration is not meaningfully related to the dependent variable (the odds of contacting PES increase by 2% for one-point increase in policy score). Finally, the parameters of GDP, expenditure on welfare and unemployment rate, lie is the negative credible interval that includes zero, which invalidates the result. Models 7 – 9 The empty model (Table A31) indicates that the random intercept variance is 0.50, hence the ICC is 0.13: 13% of the total variance of being registered with PES is explained by between-group differences. For further analysis, I specify a set of multilevel models. Goodness-of-fit DIC diagnostics indicates that the model with cross-level interactions has the highest validity (212965.3). 160 Table 11 Cross-Level Interaction Model, with ‘Registration with PES’ as a Dependent Variable Independent Variable Odds Ratio St. Deviation MCSE Median HPD [90% Credible Interval] EU Resident Status (TCNs) 2.03 0.03 0.00 2.03 2.00 – 2.07 Gender 0.81 0.00 0.00 0.81 0.80 – 0.82 Age 1.00 0.00 0.00 1.00 1.00 – 1.00 Education 0.73 0.00 0.00 0.73 0.72 – 0.74 Mainstreaming Policy Score 1.08 0.02 0.00 1.07 1.05 – 1.11 CIVIX Score 1.00 0.02 0.00 1.00 1.00 – 1.04 Unemployment Rate 1.14 0.02 0.00 1.14 1.12 – 1.17 Foreign Population in the EU 1.00 0.02 0.00 1.00 0.94 – 1.00 GDP 1.04 0.02 0.00 1.05 1.02 – 1.07 Welfare 0.94 0.01 0.00 0.94 0.92 – 0.96 EU resident status x LMS Policy Score 0.97 0.01 0.00 0.97 0.95 – 1.00 Constant 0.13 0.00 0.00 0.13 0.12 – 0.13 Random Intercept variance Var (uo). 0.27 0.19 0.01 0.23 0.07 – 0.45 Random Coefficient Variance Var (u1) 0.53 0.33 0.01 0.43 0.13 – 0.90 161 As indicated in Table 11, TCNs are on average, 103% more likely to register with PES than EU nationals. The credible interval (2.00 – 2.07) is small and does not cross zero, which validates the result. The mainstreaming policy score has a small positive effect on policy exposure: on average, for every point increase in policy score, the odds of being registered with PES increase by 1.08 (or, by 8%). This is a valid result because the parameter lies in the positive credible interval (1.05 – 1.11). However, as in the previous model, the cross-level interaction term does not have any significant effect on the dependent variable due to the negative credible interval. This does not serve as evidence that in countries with higher scores in mainstreaming, TCNs tend to register at PES more often than EU nationals. The unemployment rate is associated with a slightly higher level of policy exposure as well (14% increase). The civic integration score does not have any effect on the odds of being registered as a jobseeker. GDP is a valid predictor but its effect is negligible (4% increase in the odds of higher policy exposure). The size of the foreign population and expenditure on welfare programs lie within a negative credible interval, which indicates no effect. Models 10 – 12 The random intercept variance in the empty model with no predictors is 0.40, which means that the ICC is equal to 0.11 (Table A32). This indicates that 11% of the odds of finding a job via PES is explained by between-country differences. The DIC scores in the multilevel analysis indicate that although there is little difference in explanatory power between the random coefficient model (13219.92) and the interaction model (13218.36), the latter has a slightly better fit. I therefore report the results from the model with cross-level interactions. 162 Table 12 Cross-Level Interaction Model, with ‘Finding Employment via PES’ as a Dependent Variable Independent Variable Odds Ratio St. Deviation MCSE Median HPD [90% Credible Interval] EU Resident Status (TCNs) 0.47 0.03 0.01 0.47 0.42 – 0.52 Gender 0.88 0.02 0.00 0.88 0.85 – 0.91 Age 1.02 0.00 0.00 1.02 1.01 – 1.02 Education 0.79 0.02 0.00 0.79 0.76 – 0.82 Mainstreaming Policy Score 0.51 0.04 0.01 0.51 0.44 – 0.58 CIVIX Score 1.00 0.02 0.00 1.00 0.96 – 1.02 Unemployment Rate 1.01 0.03 0.00 1.01 0.97 – 1.06 Foreign Population in the EU 1.00 0.03 0.01 1.00 0.94 – 1.04 GDP 0.92 0.01 0.00 0.92 0.90 – 0.94 Welfare 0.87 0.01 0.00 0.87 0.85 – 0.89 EU Resident Status x Mainstreaming Policy Score 1.61 0.04 0.01 0.61 1.54 – 1.68 Constant 1.53 0.06 0.01 1.53 1.42 – 1.63 Random Intercept variance Var (uo). 0.11 0.07 0.01 0.09 0.03 – 0.21 Random Coefficient Variance Var (u1) 0.55 0.35 0.02 0.45 0.10 – 1.01 163 The random intercept model has the biggest DIC score (13252.82) and the difference between this score and two other scores is much greater than 5, which is why I excluded the model as misfit. As indicated in Table 12, both EU resident status, mainstreaming policy score as well as other country-level predictors lies in the negative credible interval, which nullifies the result. The model does not provide evidence that the variation in policy score alone affects the chances of finding a job via PES for EU residents (the model does not distinguish between EU nationals and TCNs). The interaction term, however, is meaningful: the parameter (1.61) lies in the positive range of 1.54 – 1.68. As indicated earlier in the chapter, this study relies on the ‘significance-of-the-product-term’ approach, which in Bayesian framework would mean ‘the credibility-of-the interval’ approach. The interaction term will therefore only be interpreted as the effect of employment support policy that significantly differs as a function of EU resident status, without further analysis. Models 13 – 15 Models 13 – 15 are intended to test H6 that in EU countries that invest more heavily in employment support, policy exposure leads to better employment outcomes for TCNs. The empty model (Table A33) indicates that the random intercept variance is equal to 1.13; hence, the ICC is 0.26. The use of multilevel analysis is justified because this is a strong group-level variation: 26% of the odds of finding a job via PES for TCNs is explained by between-country distinctions. The DIC diagnostics indicates that the model with random intercept has the best fit (815.75); however, the difference between this and two other multilevel models is minimal (less 164 than 5 points): 817.23 in the random coefficient model and 817.05 in the model with cross-level interactions. As indicated in Table 13, PES registration remains a meaningful predictor, which cross-validates the results displayed in single-level models. On average, TCNs who register with PES, are 283% more likely to find a job via PES. The mainstreaming policy predictor in both the random coefficient model and the model with cross-level interactions are not found to have an effect on employment outcome due to the negative credible interval; and so is the interaction term. GDP and unemployment rate are the only valid contextual predictors in the model. 165 Table 13 Random Intercept Model, with ‘Finding Employment via PES’ as a Dependent Variable (for TCNs only) Independent Variable Odds Ratio St. Deviation MCSE Median HPD [90% Credible Interval] Registration at PES (TCNs) 3.83 0.71 0.16 3.79 2.79 – 5.00 Gender 0.94 0.06 0.01 0.94 0.82 – 1.03 Age 1.01 0.01 0.00 1.01 1.00 – 1.03 Education 1.01 0.10 0.01 1.00 0.88 – 1.17 Mainstreaming Policy score 0.38 0.05 0.01 0.38 0.30 – 0.46 CIVIX Score 1.13 0.13 0.03 1.13 0.90 – 1.32 Unemployment Rate 1.26 0.13 0.03 1.06 1.06 – 1.47 Foreign Population in the EU 1.00 0.08 0.02 0.89 0.89 – 1.10 GDP 1.18 0.05 0.01 1.17 1.10 – 1.27 Welfare 0.79 0.06 0.01 0.78 0.69 – 0.88 Constant 0.04 0.00 0.00 0.04 0.03 – 0.05 Random Intercept Variance Var (uo) 0.97 0.69 0.09 0.79 0.10 – 1.84 166 Discussion This study has tested two sets of hypotheses: one measures the level of exposure to mainstreaming for TCNs and EU nationals, the other examines the relationship between policy exposure and labour market outcomes for the two groups. The principal focus is on inter-group equality: if mainstreaming works, we expect to see that TCNs keep up with EU nationals, i.e. display the same or better outcomes. Policy Exposure I test two hypotheses with regard to policy exposure and resident status: that TCNs, on average, have the same or higher level of policy exposure to labour market support as EU nationals (H1) and that country-level variation in mainstreaming strengthens this relationship (H4). The first hypothesis is supported by the findings from cross-tabulation analysis and single-level regression analysis. Findings from single-level regression models reveal that although most jobseekers, regardless of EU resident status, do not use public employment services as a main job search strategy, the percentage of unemployed TCNs who contact PES to find work is higher than the percentage of unemployed EU nationals (36% of unemployed EU nationals vs 43% of unemployed TCNs). The odds of contacting PES increase by about 30% if a person is a third-country national. Similarly, third-country nationals are more likely to register as jobseekers. Registration with PES indicates a higher level of commitment and a higher level of policy exposure. As mentioned before, it provides access to the programs and services that are unavailable to those who simply contact an employment service to make an inquiry; most of these programs require a 167 committed effort (e.g. participation in apprenticeships). Among TCNs, the total number of registered jobseekers, both employed and unemployed, is twice as high (15.3%) as the number of employed and unemployed EU nationals registered at PES (8%). Findings from regression analysis indicate that, on average, the odds of being registered as a jobseeker increase by 80% if a person is a third-country national. These findings imply that there is a much higher demand for state-funded employment support among TCNs as compared to EU nationals – possibly, because other job search strategies used by the locals, such as networking, are as easily available to foreigners. This result is mostly explained by within-country differences (87%) rather than by between-country distinctions specified in multilevel models (13%). Multilevel models reveal that there is a meaningful positive effect of mainstreaming on the chances of contacting PES, regardless of resident status: for each point increase in policy score, the odds of contacting PES increase by 2.35 (or, by 135%). Whether the impact of resident status on policy exposure is strengthened by mainstreaming is less obvious since the interaction term lies in the negative credible interval and therefore is not valid. Similarly, in the model that tests the impact of policy scope on the relationship between resident status and registration at PES, the interaction term is not valid; however, there is a small positive effect of policy scope on the chances of being registered as a jobseeker (8% increase in the odds of being registered). The hypothesis about the impact of mainstreaming on the relationship between policy exposure and employment (H4) is therefore only partly supported. Why is there such a big difference between the effect of mainstreaming on contacting PES and the effect of mainstreaming on being registered at PES (135% vs 8%)? Several explanations are possible. As reported in Table 9, the percentage of unemployed TCNs who contact PES can be three or four times higher than the overall number of TCNs who are 168 registered with PES. For example, in Austria, the percentage of unemployed third-country nationals who contacted PES is 75% and the number of registered jobseekers of immigrant background is 14%. The difference can be also explained by the fact that the number of registered jobseekers also includes employed TCNs who are looking for more job opportunities. The Relationship Between Policy Exposure and Labour Market Outcomes This study tests two hypotheses that link resident status and labour market outcomes: that third-country nationals in the EU find employment through PES as often as or more often than EU nationals (H2) and that country-level variation in mainstreaming affects this relationship (H5). As with policy exposure, single-level analysis suggests that most employed EU residents, both TCNs (93%) and non-immigrants (94%), received their current jobs using methods other than state-funded employment support. TCNs tend to benefit from employment support more than EU nationals but the difference is not meaningful (1.31%), as suggested by Table A25; there is a 17% increase in the odds of finding a job via PES for TCNs but, with a p-value equal to 0.48, the result has borderline significance. However, these findings do not suggest that TCNs have a disadvantage as compared to EU nationals. The level of finding employment via PES is about the same for both groups, which provides support for the H2 hypothesis. As with policy exposure, this result is mostly explained by within-country differences (89%) rather than by between-country distinctions (11%). There is no evidence that mainstreaming has a direct effect on finding employment via PES for EU residents, regardless of their status; however, the interaction term lies in the positive interval and although a full interpretation is not possible, it could be argued that the effect of employment support policy 169 significantly differs as a function of EU resident status. This finding provides limited support for H5. Finally, I test two hypotheses that examine a relationship between policy exposure and labour market outcomes for TCNs: that higher policy exposure to labour market support is correlated with better job market performance (H3) and that this relationship is stronger in the countries with higher policy level of mainstreaming (H6). Single-level analysis suggests a strong positive relationship between policy exposure and employment outcome for TCNs: all else equal, the odds of getting a job with assistance from PES increase by about 288% upon registration at PES. The unregistered TCNs include those individuals who did not contact PES and those who contacted PES but did not register officially. It can be argued that registration at PES, i.e. full policy exposure, is crucial for obtaining employment via PES. This finding is in line with H3. The use of multilevel analysis to test the country-specific policy effects on employment outcomes is justified due to a relatively large random intercept variance: 26% of the odds of finding a job via PES for TCNs is explained by between-country distinctions. However, despite the high variability between groups, there is no evidence that variation in policy scope affects the relationship between policy exposure and policy effect or has any effect on employment outcome. H6 is therefore not supported. It could be suggested that between-country distinctions are determined by country-level factors other than policy scope. 170 Summary of Findings What do these findings tell us about the overall impact of mainstreaming on the labour market integration of immigrants? Is mainstreaming indeed conducive to greater equality between immigrants and non-immigrants? None of the models reveal that third-country nationals have a disadvantage with regard to exposure to mainstreamed policies or to policy effect as compared to EU nationals. On the contrary, there is evidence that, on average, TCNs receive more exposure to labour market support than non-immigrants. The findings also demonstrate that immigrants’ labour market outcomes are more strongly correlated with policy exposure. To be more specific, although labour market support is not the most popular job search strategy among all the groups of jobseekers, it has a stronger effect among TCNs: individuals who register with PES to participate in labour market support programs, are more likely to find employment. These results far surpass the threshold for equality that was defined as “keeping up with the reference category of EU nationals”. We can therefore argue that on average, PES services are likely to provide an effective integration tool that can promote equality in outcomes. At the same time, we should be cautious about thinking of greater intergroup equality as a function of higher levels of mainstreaming. First, due to the small number of countries, we cannot fully assess the impact of policies – multilevel models only capture a small portion of the relationship. Second, as mentioned before, there might be other country-level factors at play that are likely to affect the outcome of interest. Although using fixed-effects single-level regression partly mitigates the problem of unobserved variation at the country level, it does so at the expense of country-level variation of interest. Nonetheless, findings from multilevel models partly support the claim that the effect of 171 immigrant status on policy exposure is stronger in countries that score higher in mainstreaming in labour market support. There is also limited evidence that the effect of finding employment through PES is stronger for third-country nationals as compared to EU nationals in countries that score higher in mainstreaming. While these findings do not allow for assertions that invoke robust causal relationships, they provide limited support for the claim that mainstreaming contributes to the variation in labour market integration of immigrants in the EU. 172 Chapter 7. Conclusion Summary of Findings Due to the challenges arising from the need to accommodate suddenly growing and increasingly diverse inflows of newcomers, European governments have been struggling to find a solution that would diversify integration policy patterns. A recent trend, mainstreaming, has been viewed by policy-makers as an alternative to traditional integration policy approaches, both ‘national models’ and cross-national policy trends. However, in some countries such as France, mainstreaming had been a dominant policy orientation for decades, prior to becoming a part of European trend. By contrast, in new EU member states such as Poland and Greece, this is a newly introduced approach to integration. There is a high-level variation across EU states with regard to policy scope and immigrants’ access to mainstreamed programs. This variation has not yet been addressed in scholarly literature. Another gap in the literature is the effect of mainstreaming. In other words, is mainstreaming conducive to better integration outcomes? My dissertation is the first study to categorize mainstreaming across multiple EU states and evaluate its impact on the labour market integration of third-country nationals. My findings can be grouped into two categories. The first set of findings in descriptive: it covers cross-national policy patterns revealed by the Index of Mainstreaming in Labour Market Support and the case studies. The second group of findings addresses the question about the impact of mainstreaming as a policy strategy. In this chapter I will, first, focus on my main findings based on these two categories. I will then outline the limitations of my research and discuss how my work contributes to the academic literature on immigrant integration more broadly. Finally, I will identify the policy implications of my study. 173 The Index of Mainstreaming in Labour Market Support and the data from the European Labour Force Survey reveal a set of policy patterns. First, while most European countries have robust eligibility rules for immigrants, allowing all categories including visa holders to enter employment support programs, spending on mainstreamed labour market support varies across EU states. In the vast majority of European states, it does not exceed 3% of GDP; the highest score are displayed by France (2.84) and Finland (2.53) and the lowest score by Greece (0.68) and Poland (0.62). Scandinavian countries such as Denmark, Sweden, and Finland, have the most generously funded employment support programs; Eastern European countries and, surprisingly, Germany, tend to have more limited spending. Second, in terms of policy scope, EU states tend to prioritize institutional and workplace training and support for apprenticeship over other employment support programs. Denmark and France have a strong preference for ALMP. Danish policies ‘activate’ jobseekers by prioritizing training programs and France tends to incentivize employers to hire jobseekers from vulnerable groups. Third, as case study analysis shows us, mainstreaming does not mark a radical policy change that is discordant with existing integration approaches in specific countries. Instead, it should be viewed as a new policy tool intended to achieve long-standing integration goals. Fourth, there is a significant difference, both in policy scope and the level of access, between ‘Western’, old EU member states and ‘the EU-10’, i.e. the new member states that joined the EU in 2004: Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia and Slovenia. Almost all the new states (with the exception of Poland and Estonia) have poorly developed labour market support policies that are also more restrictive towards immigrants. Slovakia has the lowest level of access to labour market support for 174 immigrants in the EU (only EU/EEA nationals are eligible to register with the PES); the Czech Republic, Latvia and Lithuania have restricted access to their employment support services. This trend can be confirmed if we look at other EU-10 countries that are not included in my analysis: Malta and Cyprus have restrictive regulations towards immigrants. For many new EU member states, the labour market integration of immigrants has been a fairly new topic on the policy agenda. The EU is expected to be a powerful factor in facilitating the labour market integration of immigrants (especially refugees) in Eastern European countries, through the joint initiatives of the EU PES Network, sharing best policy practices and access to funding. It is unclear whether ‘new’ EU states will catch up with the rest of the EU. The analysis of ‘direct’ mainstreaming policies indicates that there is a growing tendency toward expanding the policy scope in labour market support in accordance with EU directives on ‘activating’ employers and immigrants. This policy pattern demonstrates a growing role of inter-state cooperation in the EU; specifically, the role of the European Network of Public Employment Services and member states’ overall commitment to the Europe 2020 strategy. My second set of findings is based on the results of quantitative analysis designed to assess the relationships between resident status, policy exposure and labour market outcomes. First of all, it reveals that most jobseekers, regardless of EU resident status, do not use state-funded public employment services as a main job search strategy. However, the percentage of unemployed TCNs who contact PES to find work is higher than the percentage of unemployed EU nationals. Similarly, third-country nationals are more likely than EU nationals to register as jobseekers; in other words, they are more eager to make a committed effort to participate in mainstreamed programs and services. 175 These findings suggest that there is higher demand for state-funded employment support among third-country nationals as compared to EU nationals – possibly, because other job search strategies used by locals, such as networking, are not as easily available to immigrants. There is limited evidence that the relationship between EU resident status and policy exposure is stronger in countries with more robust level of mainstreaming; in other words, in such countries the demand for state-funded employment support is likely higher. In line with this finding, my analysis indicates that most employed EU residents, both TCNs (93%) and EU nationals (94%), have received their current job using methods other than state-funded employment support. However, these findings do not suggest that PES discriminate against TCNs or that TCNs have a disadvantage at PES as compared to EU nationals. The level of finding employment via PES is about the same for both groups. It is unclear, however, if such dynamics is more prevalent in countries with higher level of government spending on mainstreaming. Finally, and most importantly, we observe a positive relationship between policy exposure and employment outcomes for third-country nationals: all else equal, the odds of getting a job with assistance from Public Employment Services increase by about 288% upon registration at PES. The unregistered TCNs include those individuals who did not contact PES as well as those who contacted PES but did not register officially. It can be argued that registration at PES, i.e. full policy exposure, is crucial for obtaining employment via PES as it gives full access to employment support programs, although there is little evidence that this relationship is stronger in EU states that spend more on mainstreaming. My work has certain limitations stemming from methodological challenges. First of all, the analysis of country-level effects is susceptible to the number of groups (countries) in multi- 176 level regression analysis. Since in the regression analysis the number of EU states is limited to 17 countries, the impact of contextual-level predictors is difficult to trace, so the evidence in favour of country-level effects is limited. Second, as I mentioned in Chapter 3, as a method of policy impact assessment, regression analysis suffers from a number of limitations. First of all, the estimation of a country-level effects is not particularly helpful in adjusting for within-country variation. Some mainstreamed programs are implemented locally, not nationally (e.g. in Belgium, there are four regional public employment services instead of one national PES). Second, under regression analysis, identifying a clear causal link between a policy and an outcome can be problematic due a number of potential confounding variables. I have addressed this problem by adding control variables at the individual and country levels and using case study analysis. Third, multilevel analysis suffers from cluster confounding. The problem was partly addressed by using Bayesian approach and single-level analysis but it could not be eradicated due to the nature of the analysis. On the other hand, the strength of using regression analysis as a policy evaluation tool is that it allows to make comparisons across countries based on average scores (e.g. the average policy effect score across the EU and the average group-level policy effect). This is not possible in a single case study or under causal inference approach. Furthermore, regression analysis has a higher level of external validity: the inferences obtained from the survey sample can be generalized to other groups (third-country nationals and non-immigrants) in the EU. Finally, using EU nationals as a reference point is crucial for measuring the level of equality between third-country nationals and EU nationals. 177 Contributions My work has made five central contributions to the academic literature on immigrant integration. First, I have developed the Policy Index of Mainstreaming in Labour Market Support. The Index measures integration policies and programs across two categories, policy scope and policy access for various groups of immigrants. Based on the rich data from the Eurostat Labour Market Policy database, the MIPEX, and administrative sources from each EU country, the MLMS Index is the first attempt to examine and categorize mainstreamed integration policies across multiple countries. It is also the first attempt to study mainstreaming in the area of employment support. Furthermore, my dissertation identifies the legal basis for mainstreamed policies, sources of funding, and the role of institutions responsible for policy implementation. The Index is a measurement tool that enhances the understanding of the variety of mainstreamed programs. It can be used in data analysis for further policy assessment. Second, my work is the first study to address the question of whether mainstreaming in labour market support is a functional immigrant integration strategy. To tackle this question, I have conducted a cross-country quantitative analysis of policy effects. Since mainstreaming in immigrant integration is a relatively new policy strategy in the EU, it has received little scholarly attention to date. For a long time, gender and disability in education have been the only applications of mainstreaming discussed in the academic literature. Studies of immigrant integration have mostly focused on mainstreaming as a type of governance and a new concept in theoretical debates on immigration. There have been few attempts to evaluate the impact of policy mainstreaming; they are mostly confined to single case studies. Immigrant integration studies have focused predominantly on assessing the policy impact of multiculturalism, civic integration and welfare policies. In my analysis, I have examined the level of exposure of third- 178 country nationals to labour market support policies; evaluated the effect of mainstreamed labour market support policies on employment outcomes by examining the link between policy exposure and policy effect; and discussed which group of residents, EU nationals or third-country nationals, benefits more from employment assistance provided by national Public Employment Services. Although my analysis does not interpret the relationship between mainstreaming and labour market integration as causal due to data limitations and method disadvantages, it shows that the two variables are highly correlated. Third, my study uses multilevel Bayesian regression analysis in combination with single-level regression to estimate policy effects. While multilevel modelling has been effectively used in policy analysis to accommodate two-level data structure with individuals nested in groups (e.g. countries), the use of Bayesian framework instead of more conventional maximum likelihood (‘frequentist’) approach is far less common, especially in migration studies. Next, although my dissertation project focuses on integration policies, it does not ignore non-policy dimension. Human capital factors, e.g. host country relevant job skills, can be affected by relevant ALMP policies such as mainstreamed training and apprenticeship courses. The level of discrimination experienced by a newcomer is often reflective of the strength of employment incentives policy where employers are incentivized by the state to hire jobseekers from vulnerable groups. Integration policies can also facilitate immigrants’ social capital – for example, policies that encourage interaction between majority and minority groups contribute to ‘bridging’ and ‘linking’. Thus, my research can contribute to the academic literature on integration outside political science by explaining how integration policies provide a context for important non-policy determinants. 179 Last but not least, my dissertation contributes to the debate on whether and why traditional integration models can be challenged by alternative integration strategies. As previously mentioned, national models of integration varying from assimilationism to multiculturalism have been the subject of intense academic and policy debates. These debates so far have been focused on the supposed retreat from multiculturalism in liberal states and the policy conversion to ‘illiberal’, ‘forced’ civic integration under which the burden of integration lies not on the host country, but on immigrants themselves (Joppke, 2004; 2007). Where does mainstreaming fit in this discussion? On the one hand, with regard to political ideology, we clearly observe some antagonism between mainstreaming and dominant integration frameworks. Under multiculturalism and assimilationism, the immigrant has been “othered” as a “subaltern native” who is viewed as weak, disadvantaged and disempowered solely due their race or ethnicity. My study emphasizes that ‘ethnocentric’ approaches to integration often neglect privilege produced by social class. Mainstreaming, however, ‘brings class back in’. It breaks the link between race and class by addressing concerns shared by majority and minority groups. It is also more responsive to vulnerabilities stemming from gender identity, education status and physical disability than ‘ethnicity-centered’ approaches to immigrant integration. Of course, putting an emphasis on class and other non-ethnic characteristics comes at a price: mainstreaming downplays cultural and religious differences which can be an important determinant of immigrant integration. On the other hand, differences in political ideology and even policy differences do not necessarily lead to a radical institutional change. Mainstreaming does not strive to challenge long-standing policy orientations both in the area of immigrant integration and in ‘neighbouring’ policy domains such as social security policy and employment protection. We do not observe an 180 either-or situation between policy mainstreaming and the so-called ‘national models’: ‘assimilationist’ France and ‘multiculturalist’ Sweden both score high in mainstreaming. This is in line with Kymlicka and Banting’s historical-institutionalist argument on policy compatibility through institutional change mechanisms such as ‘layering’ (Kymlicka, 2010; Kymlicka & Banting, 2013). Outside the area of immigrant integration, we can see more evidence of policy compatibility or, to use a more precise term, institutional complementarity. The cases of France and Denmark demonstrate how mainstreaming and two other policy frameworks, employment protection and social security, reinforce each other’s efficiency. In France, prioritizing direct job creation in labour market support helps increase returns from the strict employment protection regime for employers. In Denmark, investing in ALMP is intended to to prevent what is perceived as abuse of the generous Nordic welfare system. This study provides evidence that EU states try out different new approaches to integration without cancelling out the old ones; and, as with civic integration and multiculturalism, we observe a mismatch between political ideology and institutional change. Policy Implications My dissertation has generated evidence that, first, there is high demand in the EU for state-provided employment support among third-country nationals and, second, that integration programs implemented by PES work for committed jobseekers. For foreigners, being registered with state-provided employment support agencies can be one of the few available job search strategies. All this provides a foundation for policy recommendations to make mainstreaming a long-term integration approach. 181 One of the main advantages of mainstreaming is its sustainability: if embedded at all stages and levels of policies, programs and projects, it may become less susceptible to political pressure. It is no secret that government decision to make cuts to integration programs can be affected by anti-immigrant sentiment (Lahav 2004, Howard 2010) or, more recently, by public concerns about COVID-battered economies. Austerity and limited budgets during the COVID era could greatly affect the scope of integration programs. Since policy mainstreaming is highly inclusive, it might be more appealing to policymakers than stand-alone integration policies. They could think of it as a cost-effective strategy that targets both immigrants and the native-born population. As a policy strategy designed to address vulnerabilities of all disadvantaged groups, including immigrants and EU nationals, mainstreaming has a potential to become an enduring policy approach. Having said that, there are some concerns that due to its focus on socioeconomic characteristics, mainstreaming could ignore or downplay vulnerabilities that are central for certain ethnic and religious groups. It is, however, possible to develop and introduce mainstreamed policies that are tailored to the needs of immigrants. Such ‘targeted’ strategies can be embedded in the wider policy realm and supplement indirect mainstreamed programs. Having ‘targeted’ programs can help policymakers keep focus on and take responsibility for immigrant integration among other competing priorities. Since mainstreaming is still at a fairly early stage in many EU states, there is a need for more comprehensive evaluation and assessment. The Policy Index of Mainstreaming does not represent a comprehensive list of mainstreamed programs and policy patterns since it only focuses on one area, labour market support. The analysis of mainstreaming in other major policy domains such as education, healthcare, and housing, could serve as a venue for future research. 182 Next, further data collection, at both national and local levels, is necessary. 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Comparative Political Studies, 44(5), 598-624. https://doi.org/10.1177/0010414010396461 Zwysen, W. (2019). Different patterns of labour market integration by migration motivation in Europe: The role for host country capital. International Migration Review 53 (1), 59 – 89. https://doi.org/10.1177/0197918318767929 198 Appendix A. Tables Table A1 Percentage of Males and Females Among EU Residents (LFS-2018) Sex Categories of EU residents Resident nationals EU nationals Third-country nationals Total Female 338, 207 51.83% 12.987 52.69% 14, 097 52.77% 365, 291 51.89% Male 314, 366 48.17% 11, 660 47.31% 12, 615 47.23% 338, 641 48.11% Total 652, 573 100% 24, 647 100% 26, 712 100% 703, 932 100% Pearson chi2(2) = 15.76 Pr = 0.00 Table A2 Parameter Estimates and Level of Significance of Bivariate Regression Analysis. The Independent Variable is MCP. Independent variables Model 1 Model 2 Model 3 Model 4 MCP and the scope of mainstreaming, with Denmark MCP and the scope of mainstreaming, without Denmark MCP and the total score of mainstreaming, with Denmark MCP and the total score of mainstreaming, without Denmark Intercept 0.59 0.34 1.96 1.34 Multiculturalism 0.004 0.05 0.09 0.22* SE 0.05 0.03 0.15 0.10 95% CI [- 0.10-0.11] [- 0.007-0.11] [- 0.23-0.41] [-0.00-0.45] Adjusted R-sq. - 0.08 0.28 - 0.05 0.24 Note. * p ≤.05. 199 Table A3 Parameter Estimates and Level of Significance of Bivariate Regression Analysis. The Independent Variable is CIVIX. Independent variables Model 1 CIVIX and the scope of mainstreaming Model 2 CIVIX and the total score of mainstreaming Intercept 0.67 2.7 CIVIX - 0.03 - 0.13 Std. Error 0.03 0.10 P-value 0.28 0.22 95% CI [- 0.06 – 0.03] [- 0.41 – 0.10] Adjusted R-sq. 0.04 0.08 Table A4 Parameter Estimates and Level of Significance of Bivariate Regression Analysis. The Independent Variable is Support for the Far-right. Independent variables Model 1 Support for the far-right and the scope of mainstreaming Model 2 Support for the far-right and the total score of mainstreaming Intercept 0.40 1.54 Support for the far-right 0.006 0.02 SE 0.006 0.02 P-value 0.35 0.50 95% CI [-0.006 – 0.12] [-0.03 – 0.06] Adjusted R-sq. - 0.004 - 0.02 200 Table A5 Public Support for the Far-Right in Europe in 2014 – 2018 Country Right-wing parties Year of parliamentary elections Votes share (%) Austria Freedom Party of Austria 2017 26 Belgium Vlaams Belang, New Flemish Alliance 2014 24 Czech Republic Freedom and Direct Democracy 2017 19 Denmark Danish People’s Party 2015 21 Estonia Conservative People’s Party, Estonian Independence Party 2015 8 Finland Finns Party 2015 18 France National Rally, Miscellaneous Right 2017 16 Germany Alternative for Germany 2017 13 Greece ANEL and Golden Dawn 2015 11 Hungary Fidesz 2018 49 Ireland N/A 2016 0 Latvia Who Owns the State? The National Alliance 2018 25 Lithuania N/A 2016 0 Luxembourg The Alternative Democratic Reform Party 2018 8 Netherlands Party for Freedom, Reformed Political Party 2017 15 Norway Progress Party, Democrats in Norway 2017 15 Poland Law and Justice, Korwin, Kukiz 2015 51 Slovakia We are Family, People’s Party Our Slovakia 2016 15 Slovenia Slovenian Democratic Party, Slovenian National Party 2014 23 Spain Vox 2015 0.23 201 Country Right-wing parties Year of parliamentary elections Votes share (%) Switzerland The Swiss People’s Party, the Ticino League, the Geneva Citizens’ Movement 2015 31 202 Table A6 The Index of Mainstreaming in Labour Market Support (2016) Country Access score Public expenditure on Training Public expenditure on Employment incentives Public expenditure on Supported employment and rehabilitation Public expenditure on Direct job creation Public expenditure on Start-up incentives Total expenditure on policies /Total country score Austria 4 0.44 0.05 0.02 0.06 0 0.57/2.28 Belgium 4 0.15 0.2 0.13 0.05 0 0.53/2.12 Czech Republic 1.524 0.01 0.07 0.1 0.06 0 0.24/0.36 Denmark 325 0.52 0.24 0.86 0 0 1.62/4.86 Estonia 4 0.08 0.02 0.07 0 0.01 0.18/0.72 Finland 4 0.47 0.1 0.12 0.13 0.1 0.92/3.68 France 3.526 0.3 0.06 0.1 0.22 0.03 0.71/2.48 Germany 4 0.19 0.02 0.02 0.02 0.01 0.27/1.08 Greece 4 0.02 0.06 0 0.07 0.02 0.17/0.68 Hungary 327 0.02 0.08 0 0.76 0.01 0.87/2.61 Ireland 4 0.19 0.04 0.1 0.16 0 0.49/1.96 Italy 4 N/A N/A 0.01 N/A N/A N/A Latvia 328 0.08 0.04 0 0.02 0 0.14/0.42 Lithuania 329 0.07 0.12 0.02 0.03 0 0.24/0.72 Luxembourg 4 0.19 0.4 0 0.16 0 0.75/3 Netherlands 4 0.07 0.04 0.36 0.02 0 0.49/1.96 Norway 4 0.12 0.11 0.15 0 0 0.38/1.52 Poland 4 0.1 0.12 0.17 0.02 0.05 0.46/1.84 24 Only permanent residents and Blue Card holders (high-skilled immigrants). 25 Excluding work permit holders. 26 All residence permit holders and work permit holders. Excluded: students, holders of a provisional authorization to work, holders of a provisional residence permit (such as asylum seekers, escort of sick person), holders of a student resident permit practising a profession in addition on their studies. 27Excluding work permit holders. 28 Generally, only TCN on permanent residence permits have equal treatment, in addition to some categories of TCN on temporary residence permits (such as spouses of persons on permanent residence permits), refugees. 29 “According to the Law on Employment Support, Lithuanian citizens and foreigners, residing lawfully in Lithuania, have equal access to public employment services. However, in practice this provision can only apply to those foreign nationals who do not need a work permit, since the latter are required to leave Lithuania upon the expiry of their permit.” (The MIPEX Lithuania). 203 Country Access score Public expenditure on Training Public expenditure on Employment incentives Public expenditure on Supported employment and rehabilitation Public expenditure on Direct job creation Public expenditure on Start-up incentives Total expenditure on policies /Total country score Portugal 4 0.2 0.16 0.01 0.04 0 0.41/1.64 Slovakia 030 0.01 0.12 0.04 0.03 0.01 0.21/0 Slovenia 33132 0.01 0.06 0 0.09 0 0.16/0.48 Spain 4 0.11 0.06 0.08 0.1 0.1 0.45/1.8 Sweden 4 0.13 0.5 0.26 0 0.01 0.9/3.6 Switzerland 4 0.18 0.08 0.23 0 0 0.49/1.96 United Kingdom 4 N/A N/A N/A N/A N/A N/A 30 “Pursuant to Act No. 5/2004 Coll. on Employment Services as amended (hereinafter as "Act on Employment Services") only citizen has access to public employment services (the citizen is however considered a citizen of the EU and EEA). Every employed TCN pays the social security contribution for unemployment insurance in compliance with Act on Social Insurance, however, in the event of unemployment, he/she is not entitled to support. In principle, if purpose of the stay ended and another permit was not arranged, migrants with temporary residence permit must leave the country within its validity and apply for a new permit from abroad. Slovakia does not help to place them in the labor market”. (The MIPEX Slovakia). 31 Excluding family reunion migrants. 32 “TCN has to have a personal work permit for three years or an indefinite period of time or has to have a status of asylum seeker to be entered in the register of unemployed persons” (The MIPEX Slovenia). 204 Table A7 The Index of Mainstreaming in Labour Market Support (2010) Country Access score Public expenditure on Training Public expenditure on Employment incentives Public expenditure on Supported employment and rehabilitation Public expenditure on Direct job creation Public expenditure on Start-up incentives Total expenditure on policies /Total country score Austria 4 0.5 0.05 0.03 0.04 0.01 0.63/2.52 Belgium 4 0.16 0.15 0.13 0.07 0 0.51/2.04 Czech Republic 1.5 0.04 0.05 0.08 0.04 0 0.21/0.31 Denmark 3 0.68 0.31 0.64 0 0 1.63/4.89 Estonia 4 0.05 0.06 0 0 0.02 0.13/0.52 Finland 4 0.51 0.12 0.09 0.09 0.02 0.83/3.32 France 3.5 0.33 0.07 0.11 0.2 0.05 0.76/2.66 Germany 4 0.27 0.09 0.03 0.05 0.08 0.52/2.08 Greece 4 0.02 0.1 0 0 0.1 0.22/0.88 Hungary 0 0.05 0.1 0 0.38 0.01 0.54/0 Ireland 4 0.43 0.05 0.1 0.24 0 0.82/3.28 Italy 4 0.15 0.14 0.01 0.01 0.02 0.33/1.32 Latvia 3 0.25 0.05 0 0.21 0 0.51/1.53 Lithuania 3 0.07 0.09 0.02 0.05 0 0.23/0.69 Luxembourg 4 0.04 0.32 0.01 0.13 0 0.5/2 Netherlands 4 0.13 0.02 0.44 0.15 0 0.74/2.96 Norway 4 0.21 0.1 0.17 0 0 0.48/1.92 Poland 4 0.04 0.21 0.21 0.04 0.1 0.6/2.4 Portugal 4 0.38 0.1 0.04 0.01 0 0.53/2.12 Slovakia 0 0.01 0.1 0.03 0.01 0.08 0.23/0 Slovenia 3 0.12 0.09 0 0.13 0.06 0.4/1.2 Spain 4 0.19 0.26 0.08 0.09 0.09 1.16/4.64 Sweden 4 0.1 0.5 0.22 0 0.02 0.84/3.36 Switzerland 4 0.2 0.07 0.21 0 0.01 0.49/1.96 United Kingdom 4 0.02 0.01 0.01 0.04 0 0.08/0.32 205 Table A8 The Index of Mainstreaming in Labour Market Support (2006) Country Access score Public expenditure on Training Public expenditure on Employment incentives Public expenditure on Supported employment and rehabilitation Public expenditure on Direct job creation Public expenditure on Start-up incentives Total expenditure on policies /Total country score Austria 4 0.39 0.05 0.03 0.04 0 0.51/2.04 Belgium 4 0.14 0.14 0.11 0.06 0 0.45/1.8 Czech Republic 1.5 0.01 0.03 0.05 0.03 0 0.12/0.18 Denmark 3 0.42 0.19 0.56 0 0 0.61/1.83 Estonia 4 0.04 0 0 0 0 0.04/0.16 Finland 4 0.37 0.14 0.09 0.08 0.02 0.7/2.8 France 3.5 0.28 0.1 0.08 0.019 0.01 0.66/2.31 Germany 4 0.35 0.05 0.03 0.09 0.12 0.64/2.56 Greece 4 0.06 0.06 0 0 0.02 0.14/0.56 Hungary 0 0.06 0.08 0 0.1 0 0.14/0 Ireland 4 0.24 0.03 0.1 0.2 0 0.57/2.28 Italy 4 0.17 0.18 0 0.01 0.04 0.4/1.6 Latvia 3 0.11 0.05 0 0.01 0 0.17/0.51 Lithuania 3 0.07 0.05 0.01 0.05 0 0.18/0.54 Luxembourg 4 0.12 0.2 0.01 0.1 0 0.43/1.72 Netherlands 4 0.1 0 0.46 0.16 0 0.72/2.88 Norway 4 0.25 0.07 0.12 0 0 0.44/1.76 Poland 4 0.1 0.03 0.17 0.02 0.04 0.35/1.44 Portugal 4 0.24 0.12 0.04 0.03 0 0.43/1.72 Slovakia 0 0.01 0.02 0.01 0.05 0.04 0.13/0 Slovenia 3 0.06 0.03 0 0.07 0.02 0.18/0.54 Spain 4 0.16 0.32 0.02 0.07 0.07 0.64/2.56 Sweden 4 0.2 0.54 0.18 0 0.03 0.95/3.8 Switzerland 4 0.2 0.07 0.19 0 0.01 0.47/1.88 United Kingdom 4 0.01 0.01 0.01 0 0 0.03/0.12 206 Table A9 Direct Mainstreaming/Targeting in Mainstreaming33 Country Some targeting Policy category Intensive targeting Policy category Overall number of policies targeted at immigrants Austria Counselling for jobseekers and enterprises by outside. LM services Administration of foreign labour legislation LM services 2 Belgium Regional employment missions-MIRE(R) LM services Vocational training (R34) Training 5 (R) Vocational integration (R) LM services Socio-professional integration bodies (R) Training Training through work in companies (R) Training Czech Republic Public works program Direct job creation Denmark Rehabilitation Supported employment and rehabilitation Guidance and upgrading Mixed 2 Estonia Job club LM services 1 Finland Integration LM services 1 France State-financed training for fragile population Training 1 Germany Perspective 50plus Training 1 Greece Subsidy program for hiring beneficiaries of local interventions Employment incentives Hungary 0 Ireland Job club LM services (cat.1) 1 Italy ESF Co-financed actions 2007-2013 - Counselling LM services Employment of immigrants Training 2 Latvia 0 33 The table lacks data on Luxembourg. 34 ‘R’ indicates regional policies. 207 Country Some targeting Policy category Intensive targeting Policy category Overall number of policies targeted at immigrants Lithuania 0 Luxembourg 0 Netherlands Minority-targeted LMP (Stimulation labour participation) Mixed 1 Norway 0 Poland 0 Portugal Special training for disadvantaged groups Training Portuguese for everyone Training Slovakia 0 Slovenia Reimbursement of employers contributions Employment incentives Public works Direct job creation 2 Spain Experimental programs for vocational integration LM services 2 Experimental programs for vocational integration (measure ES-60 regions) LM services Sweden New start jobs-1 Employment incentives Introductory activities for certain new arrivals LM services 7 New start jobs-2 Employment incentives Supported work experience with a supervisor Training Practical foundation year Training Entry recruitment incentive Employment incentives Trial Opportunity Employment incentives United Kingdom 0 208 Table A10 Mainstreaming in Labour Market Support. Examples of Programs in Denmark (2015-2016) Scope Scope Vocational training/apprenticeship Adult apprenticeship support PES subsidises companies, particularly private companies, to encourage them to commit to take on apprentices and provide training in the areas that have a shortage of workers. The regional labor market council determines in which areas of education companies can receive remuneration for apprenticeships. Duration is 3 years. Guidance and upgrading The intervention consists of two programs: 1) Education through general training aimed at providing participants with employment (e.g. interview assistance) and 2) Other courses including job-specific education/projects, apprenticeships and education of immigrants. Both types of programs can be combined. The education of immigrants is intended to provide immigrants with basic instruction in Danish language and ‘social studies’. The content of the courses is discussed during the first meeting with the municipality office representative. During participation immigrants may receive an integration allowance. They are required to join a training course within one month after the start of the program. Duration is from 6 weeks (minimum) to 26 weeks (maximum). Employment incentives Support of the disabled Personal assistance for persons with disabilities in employment. Subsidies may be granted to a business for the remuneration of a personal assistant, which can be offered to unemployed and employed people who have a need for special personal assistance on account of physical or mental impairment. Support may also be offered to employees who, on account of permanent and severe physical or mental impairment, have a need for personal assistance outside normal working hours to take part in general supplementary and further training in the relevant job. Duration depends on the contract. Wage subsidies An unemployed person may be offered employment on a fixed term contract - either with a public or a private employer. The terms and conditions offered must be those applying under the appropriate collective agreement. A wage subsidy is paid to employers who recruit the unemployed. The measure can be used for a period of up to 6 months with private employers and up to 4 month with public employers. Education may be a part of the activation offer. 209 Practical work training in enterprises The intervention may take the form of employment with private associations as well as with public employers. Activation is normally used for a period of 4 weeks if the unemployed person is eligible for unemployment insurance/benefit or if the unemployed person is eligible for cash benefits (social assistance) and has previous work experience. The period can be up to 8 weeks for recent graduates. The program is normally used for a period of up to 13 weeks with others eligible for "practical work training in enterprises". Prolonging of the duration up to 26 weeks can occur if the authorities make a decision of a prolonging due to the unemployed persons special needs. Supported employment and rehabilitation Flex jobs To give persons under the official retirement pension age (currently the age of 65 years) with significant and permanent lasting limitations in their working capacity a chance to obtain or preserve a connection to the labour market through a subsidized job, adjusted to the wageworking capacity of the person. For self- employed persons the aim is to give these persons the possibility to maintain employment in their own business. The flex job is temporary for a duration of 5 years. People over the age of 40 can get a permanently flex job after the first temporary flex job, if their working capacity is continued limited in a way, that it is not possible ever again to obtain an ordinary job. A person employed in a flexjob after the first of January 2013 receive a salary from the employer for the precise work he has performed. As a subsidy the municipality pays him a special ’flex job subsidy’ at an amount of maximum 17.312 DKR. (2014- level) per month. Regarding subsidy self-employed persons, they receive an offer of support according based on the fact that their working capacity is permanently and significantly reduced at an amount of maximum 129 286 DKR. (2014-level) per year. Wage subsidies for recipients of early retirement pension The aim is to provide early retirements to persons who are not able to maintain or obtain employment with a possibility of connection to the labour market through subsidized jobs at public or private employers. The employer receives a subsidy of ½ the wage that the employer pays to the participant. Rehabilitation and pre-rehabilitation The aim is to help people with limitations in their work capacity (physical, mental or social) re-enter the labour market. A person in rehabilitation can benefit from a package of measures including income support, preparatory training, vocational training, work experience or assistance in creating new business (interest-free loans). Maximum duration is 5 years. Pre-rehabilitation covers 3-months rehabilitation activities whose aim is to make preparations for work, education, and/or is of a clarifying character. These activities are undertaken prior to the actual rehabilitation. Resource rehabilitation courses The courses last from 1 to 5 years during which the participant is expected to improve their working capacity. The goal of the intervention is to develop the persons working capacity through a concrete and active effort. The type of activity is based on the goal jobseekers set in relation to employment and education. The intervention will often combine different kinds of offers (employment, social, health etc.). Examples: abuse recovery course, anxiety treatment, therapy etc. Maximum duration is 5 years. 210 Job assessment process The goal is to to ensure that individuals who are no longer entitled to sickness benefits but are still unable to work due to illness get help to return to the labour market. Target groups receive cash benefits to over their training courses/educational activities. Program duration is up to 2 years at a time when a person is unable to work due to illness. Direct job creation: NO Start-up incentives: NO Counselling/advisory services (language market orientation, skills assessment): NO Language training: see ‘Vocational training’ Access Permanent residents: 1 Residents on temporary work permits: 1 Residents on family reunion permit: 0 Refugees: 1 Note. The data for scope are from Labour Market Policy Statistics. Qualitative Report. Denmark 2015 by Eurostat LMP Database. Copyright 2015 by Eurostat. The data for access are from Solano, G., & Huddleston, T. Migrant Integration Policy Index 2015 (http://2015.mipex.eu/). Copyright 2015 by MIPEX. 211 Table A11 Mainstreaming in Labour Market Support. Examples of Programs in France (2017) Scope Vocational training/apprenticeship Training allowance - public system Beneficiaries receive a training allowance that is dependent upon their age and situation. Beneficiaries are: - trainees participating to training programs agreed by the state; or young people in the overseas departments. - jobseekers who have not finished their training when unemployment/training allowances cease, and who can receive the allowance for training completion (AFF). Back-to-work support - training allowance (AREF) Jobseekers receiving the Back-to-work support allowance (ARE) who undertake a training recommended by the ANPE (National Employment Agency) as a part of a personalised action program (PAP) continue to be paid the ARE during the training period up to the entitlement limit. In this case, the allowance is called the Back-to-work support – training allowance. Training cost support (AFAF) The goal is to alleviate the financial burden for jobseekers during their training. It covers the following expenses: transport, accommodation, food. Allowance for jobseekers in training (AFDEF, RFF) This allowance (AFDEF) is paid monthly to jobseekers in receipt of unemployment benefits (FR-1) who participate to a training proposed by Pôle Emploi. The objective is to obtain a qualification and find a job for which recruiting problems were identified at the local level. The allowance gives the jobseekers the opportunity to have a training for a longer period that their entitlement to unemployment benefits (3 years). Pôle Emploi training allowances The aim is to provide an income to jobseekers during the training imposed by Pôle Emploi. The amount of the allowance is equivalent to the allowance received by trainees in the public sector. It can be only paid to those who is not entitled to unemployment benefits. Trainees can also receive support for transportation fees, accommodation and food. Maximum duration is 36 months. Courses for specific groups (IRILL) The IRILL program (integration, reintegration and fight against illiteracy) combines a number of training actions for 2 groups with particular difficulties with labour market and social integration: prisoners and illiterate people. These actions remain under the responsibility of the State (i.e. not decentralised). Some trainees may receive a training allowance of a temporary delay allowance (FR-89). The illiteracy strand of the program is ending and is replaced by FR-104 (Key competences) which started in 2009. The measure therefore only remains for the prisoners. 212 Training agreements (AFC) Training agreements are offered to jobseekers who need to reinforce their professional capacities. Pôle Emploi provides financial help to training institutions offering the actions necessary to match the qualification needs identified at the local level or in promising sectors. Up until 2008, this support was financed by UNEDIC. The support covers training fees, registration and administrative fees. The maximum number of hours for these actions are fixed at the regional level. Voluntary contract for integration The aim is to promote the social and vocational integration of young people (18-22 year-old) without skills/job, facing difficulties at school or particular problems with integration. The contract is established under public law which gives a general and vocational training. It is implemented under the joint supervision of the Ministries of Defence, Labour and Law. The beneficiary signs a contract for 6 to 12 months which is renewable. Key competences The aim is to help disadvantaged people to get basic skills. The program is the result of the reform of the Ministry of Labour’s policy to promote access to vocational training for low-skilled individuals. It includes measures to fight against illiteracy. The State contributes to the provision of training in the following key competencies: reading and writing skills, basic skills in mathematics and sciences, use of information and communication technologies. Second chances schools The program allows young people without diploma, and without vocational skills to build their social and professional integration project. These schools propose an individualized guidance pathway over several months. It is organized around 3 pillars: courses in the basic knowledge, training in enterprises (which is the main focus of the measure) and sociocultural and sportive activities. FNE conventions on training and retraining The aim is to help employees at risk of lay-off to maintain the jobs threatened by new technology through continuing vocational training. The training programs promote the recognition of qualifications or validation of prior experience. Training actions prior to recruitment This training support provided under the Back-to-Work Support Plan can be granted for a training action which constitutes a prerequisite in the recruitment for a vacancy job proposed by Pôle Emploi. The employer commits themselves to sign a work contract with the young jobseekers once they reach the required qualification level (open-ended contract, fixed-term contract of 6 months). Beneficiaries are jobseekers requiring an adaptation through an internal or external training in order to comply with an identified job offer. Operational preparation for employment (POE) The aim is to help jobseekers to have the necessary vocational skills and competencies in order to take a vacancy in the company. The POE program finances a training which provides the necessary skills to take up a sustainable job matching a vacancy in Pôle Emploi. The POE is targeted at jobsekeers registered at Pôle Emploi and to whom the Pôle Emploi advisor proposes a stable employment which requires a preliminary training. 213 Exemptions and subsidies for recruitment of apprentices The aim is to encourage the recruitment of young people on apprenticeship contracts so that they can obtain a recognized vocational qualification. Beneficiaries are young people who are unemployed, inactive or have low qualifications. The apprenticeship contract is an alternance training contract lasting at least as long as the term of the training which is the object of the contract (1-3 years). The Voluntary contract for integration The Voluntary contract for integration (CVI) is a contract established under public law which gives a general and vocational training. Beneficiaries are 18-22 year-old without skills/job, facing difficulties at school or particular problems at the level of social and vocational integration. Duration: maximum 24 months. Employment incentives A job for the future (market) The job for the future (market) is an agreement between the State, the employer in the market sector and the jobseeker. Employers are mainly private employers. The agreements are associated with working contracts of 35 hours per week (unless exception is granted) which can either be temporary or open-ended contracts. The agreement foresees the payment by the State of a financial support to the employer hiring the young jobseeker. The support is paid for a planned duration of 1 to 3 years provided that the agreement is not prematurely stopped. The support corresponds to 35% of the gross hourly minimum wage. Generation contract Financial support provided to enterprises (private employers) recruiting a young person aged <26 or employees aged >57 in an open-ended contract and maintaining in employment (or recruiting) an older person. Duration: 3 years. Digressive temporary allowance The aim is to encourage unemployed individuals to accept a job with a lower salary. A compensatory allowance can be granted to redundant persons taking on a permanent job (or a temporary job of more than 6 months – since 2008) with a salary lower than in their previous employment. The new job must be with a different company, within maximum one year of the redundancy. The allowance is paid for a maximum of 2 years. Single inclusion contract This is an agreement between the State, the enterprise (in the market sector) and the jobseeker. It is applied in the market sector (mainly in private enterprises). The State provides a financial support to employers hiring jobseekers (either on an open-ended or fixed-term contract). The duration of the support can be between 6 months and 2 years. In some particular cases however (for instance for ex- prisoners in a re-integration process), the duration can be 3 months. The amount is calculated based on the minimum salary (not more than 47% of the gross hourly smic) and work duration (up to 35 hours per week). Reduced social contributions for the recruitment of a youth on a permanent contract When hiring young <26, the employer does not pay contribution of unemployment insurance during 3 months (4 months for enterprises with < 50 employees). The condition is that the contract continues after the trial period. Duration: 3 months. 214 Economic integration enterprises An integration enterprise is a productive unit which accommodates people with severe problems, youths or adults, for up to 2 years in order to allow them to (re)adjust to working life before moving on to the job market. Intermediary associations The State subsidises the setting up of organisations that help find work for long-term unemployed and all problem groups. These organisations bring together employers (mostly households) and the unemployed for very short-term jobs. They also provide a point of contact and support for people having problems of reintegration, as well as a follow-up of their professional and social paths. Employment incentives Enterprises (certified by Pôle Emploi) provide temporary jobs for people faced with difficulties in the labour market. Job contracts can last up to 24 months. Professionalisation contract Beneficiaries are young people aged between 16-25 who dropped out school without any recognized diploma and jobseekers > 45. The contract is available since 2004 to all employers who have to participate in lifelong-learning programs. Duration is 6-12 months. Supported employment and rehabilitation Supported contracts for disabled (ESAT) The aim is to guarantee a minimal income to people with disabilities working in a sheltered environment. State support is equal to 50% of the minimum income when the part financed by ESAT equals 5-20% of the minimum income; is reduced when the part financed by ESAT is higher than 20% of the minimum income. Adapted firms The aim is to access to regular employment for people with disabilities. The agreement of the adapted firms is based on a 3-year contract signed with the State, which determines the reception, guidance and follow-up of the disabled workers. The agreement includes a specific subsidy paid to employers for additional costs incurred by the employment of disabled people; and financial support for each worker. Direct job creation A job for the future (non-market) This measure is a tripartite agreement signed between the State, the employer in the non-market sector and the jobseeker. The agreement is prescribed by a local centre (FR-44). Employers in the non-market sector are the local authorities, public bodies (except in the education sector) as well as the associations. The agreements are associated with employment contracts of 35 hours per week (except in special cases) which can either be fixed-term or open-ended. According to the agreement, the State pays a financial support to employers hiring young jobseekers. This is paid for a period of one to three years, provided that the agreement does not stop prematurely. The financial support paid to the employer corresponds to 75% of the gross hourly minimum wage. 215 Single inclusion contract The aim is to promote the vocational integration of people aged 16-65 facing difficulties to access to employment. This is an agreement between the State, the non-market sector (association, local authorities, public bodies) and the jobseeker. The State provides financial support to employers hiring jobseekers (either on an open-ended or fixed-term contract). The duration of the support can be between 3 months and 2 years. A job for the future (non-market) The aim is to promote the integration into the labour market of unskilled or low-skilled young people as well as young people living in sensitive urban areas or disadvantaged rural areas. This measure is a tripartite agreement signed between the State, the employer the non-market sector (local authorities, public bodies, associations) and the jobseeker. The agreement includes an employment contract of 35 hours per week which can either be fixed-term or open-ended. The financial support paid to the employer corresponds to 75% of the gross hourly minimum wage. Integration workshops/initiatives (ACI) The aim is to recruit, support, train people facing social and professional problems in the job market. The ACI organizes the follow-up, the guidance and the training in order to promote social integration and sustainable vocational integration. ACI can be organized on a case-by-case basis or on a permanent basis. Start-up incentives Aid for the unemployed setting-up or rescuing a company (ACCRE) The aim is to help jobseekers to start/take over a company, or to take up a non-salaried profession. Beneficiaries are jobseekers receiving benefits or eligible for benefits not receiving benefits registered for more than 6 months in the last 18 months. The aid for the unemployed setting-up or rescuing a company. It consists of social contributions exemption for 1 year. (up to 120% of the minimum wage). Since 2005, the duration of exemption is increased up to 24 months. Support for rescuing or creating a company (ARCE) The aim to help unemployment benefit recipients to create their own business. The amount corresponds to 50% of the remaining claims when starting the activity. New support to create/rescue a company (NACRE) This intervention proposes an intensive assistance of at least 3 years: . phase 1: support to finalise the company creation/rescuing project (maximum 6 months) - phase 2: systematic assistance to establish quality relationship with banks (maximum 6 months) in order to obtain a zero rate loan as well as an additional bank loan - phase 3: post-creation support extending over 3 years after the set up of rescue of the company, in order to help the new manager in terms of management choices and promoting the development of the company. Counselling/advisory services (language market orientation, skills assessment) National interprofessional agreement for youth Enhanced follow-up of young people under 26 y.o. without higher education searching for a job, in 3 phases: - Diagnostic 216 - Definition of a plan - Follow-up during the job. Duration: 8 months Follow-up level 2 – accompanied Physical interviews with counsellors, phone calls and emails. Subsidy is provided. Duration: continuously available. Follow-up level 3 – intensive Jobseekers have frequent contacts with their counsellors. Physical interviews are preferred. Duration: continuously available. Access Permanent residents: 1 Residents on temporary work permits: 1 Residents on family reunion permit: 1 Refugees: 1 Note. The data for scope are from Labour Market Policy Statistics. Qualitative Report. France 2017 by Eurostat LMP Database. Copyright 2017 by Eurostat. The data for access are from Solano, G., & and Huddleston, T. Migrant Integration Policy Index 2015 (http://2015.mipex.eu/). Copyright 2015 by MIPEX. 217 Table A12 Mainstreaming in Labour Market Support. Examples of Programs in Slovakia (2017) Scope Vocational training/apprenticeship Education and preparation for the labour market Education and preparation for the labour market based on the evaluation of work experience, professional skills, level of acquired education and health ability for work. The Office may provide education and preparation for the labour market for a jobseeker and a person interested in employment primarily under the regional, national or pilot projects of education and preparation for the labour market, approved by the Ministry or Centre. The Centre and the Office may elaborate the projects and programs of education and preparation for the labour market. The Labour Office may, by written agreement, provide a contribution toward education and preparation for the labour market of the registered jobseeker amounting up to 100% of the cost of the education and preparation for the labour market. The Labour Office may, by written agreement, refund up to 100 % of the cost per one education activity in the course of two years during which he has been registered as a person interested in employment. Vocational education and training of employees The employer provides vocational education and training of staff for the purposes of this act in order to ensure sustainable employment. The staff training is provided in the form of general or specific training, pursuant to specific regulations. Employment incentives for jobseekers Contribution for commuting to work The contribution for commuting to work shall be provided monthly for travel to work to cover a part of an employee’s travel costs for commuting from his/her place of permanent residence or place of temporary residence to the place of employment specified in his/her employment contract and back if the employee was a jobseeker registered in the register of jobseekers for at least three months who was removed from the register of jobseekers for the reason in section 36(1)(a) and if he/she applies for the contribution in writing no later than one month after starting employment. The contribution can be provided again after two years from the end of provision. The contribution shall be provided to an employee for at most six months from the start of employment Relocation allowance when taking up a new job Contribution for relocation is a compensation of documented expenses occurring due to relocating from the place of permanent residence to the new job location, if the new location is at least 50 km distant from the place of previous permanent residence. The Labour Office may grant the contribution once in two years in the form of a lump sum not exceeding 1327,76 EUR. 218 Supported employment and rehabilitation Contribution for establishing a protected workshop and protected workplace The contribution for establishing a protected workshop or protected workplace shall be granted to the employer employing the job seeker who is a disabled citizen in a generated job in the sheltered workshop or sheltered workplace. The contribution per job generated in the sheltered workshop or sheltered workplace shall be share of the amount of the total price of labour calculated from the average wage of an employee in the Slovak Republic’s economy for the first till the third quarters of previous calendar year. The amount of the contribution shall depend on the type of region eligible for the provision of the State aid and the average rate of registered unemployment of the district and on the legal status and the subject of business of the employer. The sheltered workshop or sheltered workplace shall be established for at least two years. Contribution for assistant job The Labour Office shall grant to employer or to a self-employed person, who is disabled citizen a contribution for activities of the assistant at work. The assistant at work is defined as the employee or the natural person that provide assistance to an employee or to employees, who are disabled citizens in their execution of employment, the operation or performance of self-employment, training or assistance in preparation for vocational assertion of disabled citizen and in their execution of personal needs at activities specified above. The assistant at work must have completed 18 years of age and legal capacity. Direct job creation Voluntary works are defined as a form of activation activity of the job seeker, executed by performing voluntary activities in order to gain practical experience for needs of the labour market. Activation activity shall be executed in the course of six calendar months in the duration of at least 20 hours per weekend at a legal person or natural person that its voluntary activities doesn ́t realize for purpose to reach profit. The Labour Office shall, during the voluntary works, grant a lump-sum contribution for job seeker in amount of subsistence minimum for one major natural person according to special regulation. Contribution for activation activity executed by performing voluntary activities consists of contribution in compensation for parts of the expenses relates with carrying on voluntary service and of part of the total price of work of the employee organising the voluntary service. Start-up incentives The contribution to self-employment may be granted by written request of the job seeker who has been on file for at least three months in the register of job seekers, providing that she/he will commence, and continue performing self-employing activities for at least three years. The contribution to self-employment may be granted to cover documented expenses in link with self-employing activities and it is derived from the amount of the total price of labour calculated from the average gross wage of an employee in the Slovak Republic’s economy for the first till the third quarters of previous calendar year. The sum of the contribution depends on the average of unemployment rate in the relevant district. Counselling/advisory services (language market orientation, skills assessment) Information and advisory services Assistance with: a) career choice, b) career change; c) employee selection; d) adaptation of the employee in their new job. 219 Professional consultancy Professional consultancy is the activity executed toward resolving problems in link with the vocational assertion of job seekers, toward harmonizing the conditions of his personality with the requirements of specific employment, toward influencing the applicant’s decisions and conduct, and toward the social and working adaptation of the job seeker. Mediation of employment Mediation of employment is an activity executed for the following purposes: a) Seeking out jobs and offering suitable employment to job seekers and to persons interested in employment, b) Seeking out and matching suitable employees with employers. Mediation of employment includes information and advisory services. Language training: NO Access Permanent residents: 0 Residents on temporary work permits: 0 Residents on family reunion permit: 0 Refugees: 0 Note. The data for scope are from Labour Market Policy Statistics. Qualitative Report. Slovakia 2017 by Eurostat LMP Database. Copyright 2017 by Eurostat. The data for access are from Solano, G., & Huddleston, T., Migrant Integration Policy Index 2015 (http://2015.mipex.eu/). Copyright 2015 by MIPEX. 220 Table A13 Cross-tabulation Analysis of Contacting PES by Groups of EU Residents Contacted PES to find work Categories of EU residents Country nationals EU nationals Third-country nationals Total No 24, 872 64.09% 1, 511 66.39% 2,012 57.37% 28, 395 63.68% Yes 13, 935 35.91% 765 33.61% 1, 495 42.63% 16, 195 36.32% Total 38, 807 100% 2, 276 100% 3, 507 100% 44,590 100% Pearson chi2(5) = 70.41, Pr = 0.00 221 Table A14 Logistic Regression Analysis of the Odds of Contacting PES for Three Groups of EU Residents Independent variable 𝜷 SE 𝜷 Z Odds Ratio Percentage change in odds EU nationals -0.09** 0.05 -2.03 0.91 -8.9 Third-country nationals 0.25*** 0.04 6.95 1.29 29 Age 0.007*** 0.00 9.46 1.00 0.7 Gender -0.04*** 0.02 -2.26 0.96 -4.4 Education -0.16*** 0.01 -12.19 0.85 -14.6 Constant - 0.70 0.04 -18.68 0.50 Model 𝝌2 327 P < 0.01 Pseudo R2 0.00 N 44,425 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 Note: The dependent variable is ‘contacting PES to find work’, 0 = ‘No’, 1 = ‘Yes’. 222 Table A15 Cross-tabulation Analysis of Registration at PES by Groups of EU Residents Registration at PES Categories of EU residents Country nationals EU nationals Third-country nationals Total No 429, 880 92.16% 17, 506 92% 17, 937 84.75% 465, 323 91.84% Yes 36, 568 7, 84% 1, 525 8.01% 3, 227 15.25% 41, 320 8.16% Total 466, 448 100% 19, 031 100% 21, 164 100% 506, 643 100% Pearson chi2(5) = 1,500, Pr = 0.00 223 Table A16 Logistic Regression Analysis of the Odds of Being Registered at PES Independent variable 𝜷 SE 𝜷 Z Odds Ratio Percentage change in odds EU nationals -0.02 0.03 -0.64 0.98 -1.8 Third-country nationals 0.59*** 0.02 29.02 1.79 79.8 Age -0.02*** 0.00 -47.23 0.98 -1.5 Gender -0.20*** 0.01 -19.81 0.81 -18.7 Education -0.36*** 0.00 -41.79 0.75 -24.9 Constant -1.41 0.02 -81.67 0.24 Model 𝝌2 5257.16 P < 0.01 Pseudo R2 0.02 N 501, 334 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 Note: The dependent variable is ‘registered at PES’, 0 = ‘No’, 1 = ‘Yes 224 Table A17 Cross-tabulation Analysis of Involvement of PES in Finding the Present Job by Employed Groups of EU Residents Involvement of PES in Finding the Present Job Categories of EU residents Country nationals EU nationals Third-country nationals Total No 35, 227 94.30% 2, 533 95.44% 2, 520 93.13% 40, 280 94.30% Yes 2, 130 5.70% 121 4.56% 186 6.87% 2, 437 5.70% Total 100% 100% 100% 100% Pearson chi2(2) = 13.3478, Pr = 0.00 225 Table A18 Logistic Regression Analysis of Involvement of PES in Finding the Present Job by Employed Groups of EU Residents Independent variable 𝜷 SE 𝜷 Z Odds Ratio Percentage change in odds EU nationals -0.22** 0.09 -2.33 0.80 -20.0 Third-country nationals 0.14 0.08 1.81 1.16 15.5 Age 0.01*** 0.00 7.03 1.01 1.2 Gender -0.08** 0.04 -1.95 0.92 -7.9 Education -0.18*** 0.03 -6.27 0.83 -16.4 Constant -2.98 0.08 -38.65 0.05 Model 𝝌2 111.62 P < 0.01 Pseudo R2 0.00 N 42,586 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 Note: The dependent variable is ‘involvement of the public employment office at any moment of finding the present job’, 0 = ‘No’, 1 = ‘Yes 226 Table A19 Logistic Regression Analysis: the Impact of Policy Exposure on Labour Market Outcomes for TCNs Independent variable 𝜷 SE 𝜷 Z Odds Ratio Percentage change in odds Registered at PES 1.36*** 0.18 7.71 3.88 288 Age 0.00 0.00 0.85 1.00 0.6 Gender 0.09 0.16 0.55 1.09 9.1 Education -0.08 0.10 -0.85 0.91 -8.3 Constant -3.01 0.31 -9.67 0.05 Model 𝝌2 53.78 P < 0.01 Pseudo R2 0.04 N 2, 548 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 Note: The dependent variable is ‘involvement of the public employment office at any moment of finding the present job’, 0 = ‘No’, 1 = ‘Yes’. 227 Table A20 Logistic Regression Analysis: the Impact of Policy Exposure on Labour Market Outcomes for Country Nationals Independent variable 𝜷 SE 𝜷 Z Odds Ratio Percentage change in odds Registered at PES 0.85*** 0.06 14.95 2.35 135.0 Age 0.006*** 0.00 5.35 1.00 1.0 Gender -0.10*** 0.05 -2.28 0.90 -10.0 Education -0.13*** 0.03 -3.98 0.88 -11.8 Constant -3.05 0.08 -36.19 0.05 Model 𝝌2 277.83 P < 0.01 Pseudo R2 0.02 N 34,761 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 Note: The dependent variable is ‘involvement of the public employment office at any moment of finding the present job’, 0 = ‘No’, 1 = ‘Yes’. 228 Table A21 Logistic Regression Analysis: Impact of Policy Exposure on Labour Market Outcomes for Intra-EU Migrants Independent variable 𝜷 SE 𝜷 Z Odds Ratio Percentage change in odds Registered at PES 0.97*** 0.27 3.51 2.62 162.6 Age -0.00 0.00 -0.44 1.00 -0.4 Gender 0.20 0.19 1.01 1.22 31.5 Education -0.31*** 0.13 -2.43 0.73 -26.9 Constant -2.77 0.37 -7.22 0.07 Model 𝝌2 18.46 P < 0.01 Pseudo R2 0.02 N 2, 338 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 Note: The dependent variable is ‘involvement of the public employment office at any moment of finding the present job’, 0 = ‘No’, 1 = ‘Yes’. 229 Table A22 Cross-tabulation Analysis: Contacting PES by Groups of EU Residents Contacted PES to find work Categories of EU residents EU nationals Third-country nationals Total No 26, 383 64.22% 2,012 57.37% 28, 395 63.68% Yes 14, 700 35.78% 1, 495 42.63% 16, 195 36.32% Total 3, 507 100% 3, 507 100% 44,590 100% Note. Pearson chi2(1) = 65.5112, Pr = 0.000 230 Table A23 Logistic Regression Analysis: the Odds of Contacting PES for Two Groups of EU Residents Independent variable 𝜷 SE 𝜷 Z Odds Ratio Percentage change in odds Third-country nationals 0.26*** 0.04 7.10 1.29 29.1 Age 0.007*** 0.00 9.44 1.007 0.7 Gender -0.04*** 0.02 -2.22 1 -2.2 Education -0.16*** 0.01 -12.25 0.85 -11.6 Constant -0.70 0.04 -18.82 0.50 Model 𝝌2 324.32 P < 0.01 Pseudo R2 0.00 N 44,425 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 Note: The dependent variable is ‘contacting PES to find work’, 0 = ‘No’, 1 = ‘Yes 231 Table A24 Cross-tabulation Analysis: Registration at PES by Groups of EU Residents Registration at PES Categories of EU residents EU nationals Third-country nationals Total No 447, 386 92.15% 17, 937 84.75% 465, 323 91.84% Yes 38, 093 7.85% 3, 227 15.25% 41, 320 8.16% Total 485, 479 100% 21, 164 100% 506, 643 100% Note. Pearson chi2(1) = 1.5, Pr = 0.000 232 Table A25 Logistic Regression Analysis: the Odds of Being Registered at PES Independent variable 𝜷 SE 𝜷 Z Odds Ratio Percentage change in odds Third-country nationals 0.58*** 0.02 29.11 1.80 79.9 Age -0.02*** 0.00 -47.24 1.00 -1.5 Gender -0.21*** 0.01 -19.80 0.81 -18.7 Education -0.36*** 0.006 -41.81 0.75 -24.9 Constant -1.42 0.02 -82.08 0.24 Model 𝝌2 5108.42 Pseudo R2 0.02 N 501, 334 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 Note: The dependent variable is ‘registered at PES’, 0 = ‘No’, 1 = ‘Yes’. 233 Table A26 Cross-tabulation Analysis of Involvement of PES in Finding the Present Job by Groups of EU Residents Involvement in PES Categories of EU residents EU nationals Third-country nationals Total No 37, 702 94.38% 2, 578 93.07% 40, 280 94.30% Yes 2, 245 5.62% 192 6.93% 2, 437 5.70% Total 39, 947 100% 2, 770 100% 42, 717 100% Note. Pearson chi2(1) = 8.28, Pr = 0.004 234 Table A27 Logistic Regression Analysis of Employment Support Policy Impact on EU Residents Independent variable 𝜷 SE 𝜷 Z Odds Ratio Percentage change in odds Third-country nationals 0.16* 0.08 -2.00 1.17 17.1 Age 0.01*** 0.00 6.94 1.01 1.2 Gender -0.08 0.04 -1.98 0.92 -7.9 Education -0.18*** 0.03 -6.32 0.83 -16.5 Constant -3.00 0.10 -38.77 0.05 Model 𝝌2 105.85 Pseudo R2 0.00 N 42.586 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 Note: The dependent variable is ‘involvement of the public employment office at any moment of finding the present job’, 0 = ‘No’, 1 = ‘Yes’ . 235 Table A28 Logistic Regression Analysis of the Impact of Policy Exposure on Employment Outcomes for TCNs Independent variable 𝜷 SE 𝜷 Z Odds Ratio Percentage change in odds Registered at PES 1.35*** 0.18 7.71 3.88 288.0 Age 0.006 0.007 0.85 1.00 0.6 Gender 0.09 0.16 0.55 1.09 9.1 Education -0.09 0.10 -0.85 0.91 -8.3 Constant -3.01 0.31 -9.67 0.05 Model 𝝌2 53.78 Pseudo R2 0.04 N 2, 548 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 Note: The dependent variable is ‘involvement of the public employment office at any moment of finding the present job’, 0 = ‘No’, 1 = ‘Yes’. . 236 Table A29 Logistic Regression Analysis of the Impact of Policy Exposure on Employment Outcomes for EU Nationals Independent variable 𝜷 SE 𝜷 Z Odds Ratio Percentage change in odds Registered at PES 0.86*** 0.06 15.46 2.37 137.1 Age 0.009*** 0.002 5.15 1.00 0.9 Gender -0.09** 0.04 -1.97 0.92 -8.4 Education -0.14*** 0.03 -4.54 0.87 -13.0 Constant -3.03 0.08 -37.00 0.05 Model 𝝌2 294.19 Pseudo R2 0.02 N 37,162 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 Note: The dependent variable is ‘involvement of the public employment office at any moment of finding the present job’, 0 = ‘No’, 1 = ‘Yes’. Table A30 Random Intercept Variance in the Empty Model with ‘Contacting PES’ as a Dependent Variable Estimate Std. Error 95% Confidence Interval Country Var (uoj) 0.55 0.19 0.28 – 1.07 Model 𝝌2 6375.58 P < 0.01 237 Table A31 Random Intercept Variance in the Empty Model with ‘Registration with PES’ as a Dependent Variable Estimate Std. Error 95% Confidence Interval Country Var (uoj) 0.50 0.17 0.25 – 1.00 Model 𝝌2 15502.35 P < 0.01 Table A32 Random Intercept Variance in the Empty Model with ‘Finding a Job via PES’ as a Dependent Variable Estimate Std. Error 95% Confidence Interval Country Var (uoj) 0.40 0.14 0.20 – 0.80 Model 𝝌2 788.78 P < 0.01 Table A33 Random Intercept Variance in the Empty Model with ‘Finding Employment via PES’ as a Dependent Variable (for TCNs only) Estimate Std. Error 95% Confidence Interval Country Var (uoj) 1.13 0.46 0.50 – 2.53 Model 𝝌2 123.43 P < 0.01 238 Table A34 MLE Multilevel Model with Contacting PES as a Dependent Variable Independent Variable Odds Ratio St. Error Z 95% Credible Interval Intra-EU Migrants 1.12 0.32 0.40 0.64 – 1.95 Third-Country Nationals 2.07*** 0.44 3.46 1.37 – 3.15 Mainstreaming Policy Score 1.06 0.40 0.15 0.50 – 2.20 Intra-EU Migrants x Mainstreaming Policy Score 0.95 0.13 -0.39 0.72 – 1.24 TCNs x Mainstreaming Policy Score 0.81** 0.08 -2.32 0.66 – 0.97 Gender 1.00 0.025 -0.58 0.94 – 1.03 Age 1.00*** 0.00 9.18 1.00 – 1.01 Education 0.83*** 0.01 -11.48 0.80 – 0.85 CIVIX Score 1.00 0.04 -0.20 0.92 – 1.07 Unemployment Rate 0.91 0.05 -1.50 0.81 – 1.03 Foreign Population in the EU 1.18*** 0.04 5.27 1.10 – 1.25 GDP 0.91*** 0.03 -3.00 0.86 – 0.97 Welfare 0.95* 0.03 -1.80 0.89 – 1.00 Constant 0.73 0.49 -0.42 0.21 – 2.68 Random Intercept Variance Var (uo). <0.00 <0.00 Random Coefficient Variance Var (u1) 0.02 0.00 0.00 0.36 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 239 Table A35 MLE Multilevel Model with Registering with PES as a Dependent Variable Independent variable Odds Ratio St. Error Z 95% Credible Interval Intra-EU Migrants 3.42*** 0.63 6.67 2.38 – 4.90 Third-Country Nationals 2.6*** 0.30 8.54 2.08 – 3.23 Mainstreaming Policy Score 1.80 0.70 1.41 0.80 – 3.86 Intra-EU Migrants x Mainstreaming Policy Score 0.64*** 0.05 -5.28 0.54 – 0.75 TCNs x Mainstreaming Policy Score 0.89** 0.04 -2.42 0.80 – 0.97 Gender 0.81*** 0.00 -17.98 0.79 – 0.83 Age 1.00*** 0.00 -43.01 1.00 – 1.00 Education 0.73*** 0.00 -40.46 0.71 – 0.74 CIVIX Score 1.14** 0.07 2.06 1.00 – 1.30 Unemployment Rate 1.05 0.07 0.84 0.93 – 1.20 Foreign Population in the EU 1.00 0.03 -0.84 0.91 – 1.04 GDP 0.91 0.05 -1.62 0.83 – 1.00 Welfare 0.94 0.05 -1.33 0.85 – 1.03 Constant 0.16 0.14 -2.01 0.03 – 0.95 Random Intercept Variance Var (uo). <0.00 <0.00 Random Coefficient Variance Var (u1) 0.02 0.00 0.00 0.04 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 240 Table A36 MLE Multilevel Model, with ‘Finding Employment via PES’ as a Dependent Variable Independent variable Odds Ratio St. Error Z 95% Credible Interval Employed Intra-EU Migrants 0.73 0.46 -0.50 0.22 – 2.49 Employed Third-Country Nationals 0.47 0.20 -1.70 0.20 – 1.12 Mainstreaming Policy Score 0.57 0.20 -1.57 0.30 – 1.15 Employed Intra-EU Migrants x Mainstreaming Policy Score 0.93 0.29 -0.22 0.51 – 1.70 Employed TCNs x Mainstreaming Policy Score 1.60** 0.33 2.21 1.05 – 2.37 Gender 0.92 0.44 -1.77 0.83 – 1.00 Age 1.02*** 0.00 8.20 1.01 – 1.02 Education 0.78*** 0.03 -7.29 0.73 – 0.83 CIVIX Score 0.94 0.04 -1.62 0.87 – 1.01 Unemployment Rate 0.99 0.06 -0.24 0.88 – 1.10 Foreign Population in the EU 1.02 0.03 0.71 0.96 – 1.08 GDP 0.91*** 0.03 -2.89 0.86 – 0.97 Welfare 0.86 0.03 -4.88 0.81 – 0.92 Constant 1.40 0.90 0.53 0.40 – 4.90 Random Intercept Variance Var (uo). <0.00 <0.00 0.00 – 0.33 Random Coefficient Variance Var (u1) 0.01 0.00 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 241 Table A37 MLE Interaction Model, with ‘Finding Employment via PES’ as a Dependent Variable (for TCNs only) Independent variable Odds Ratio St. Error Z 95% Credible Interval Registration with PES 9.30** 9.40 2.21 1.28 – 67.5 Mainstreaming Policy Score 0.24 0.22 -1.53 0.04 – 1.50 Registration at PES x Mainstreaming Policy Score 0.68 0.30 -0.86 0.28 – 1.64 Gender 1.00 0.19 0.01 0.69 – 1.46 Age 1.01 0.00 1.16 1.00 – 1.03 Education 0.96 0.12 -0.34 0.76 – 1.22 CIVIX Score 1.08 0.16 0.49 0.80 – 1.45 Unemployment Rate 1.23 0.20 1.23 0.88 – 1.70 Foreign Population in the EU 0.95 0.07 -0.75 0.82 – 1.09 GDP 1.10 0.15 0.65 0.83 – 1.45 Welfare 0.73*** 0.09 -2.65 0.58 – 0.92 Constant 0.86 2.11 -0.06 0.00 – 107.2 Random Intercept Variance Var (uo). <0.00 <0.00 0.02 – 0.23 Random Coefficient Variance Var (u1) 0.07 0.04 * P-value<0.1, ** p-value<0.05, ***p-value<0.01 242 Table A38 Summary of Findings from MLE Multilevel Models Models Tables Findings Model 1 Table A34 EU resident status is a significant predictor: on average, TCNs are 2.07 times (107%) more likely than country residents to contact PES when they seek employment. The effect of mainstreaming on contacting PES: not significant Interaction effect (product term): is significant for TCNs only. Model 2 Table A35 EU resident status is a significant predictor: on average, TCNs are 2.60 times (160%) more likely than country residents to register with PES when they seek employment. The effect of mainstreaming on PES registration: not significant Interaction effect (product term): significant Model 3 Table A36 EU resident status: not significant The effect of mainstreaming on finding employment via PES: not significant Interaction effect (product term): significant for TCNs Model 4 Table A37 EU resident status is a significant predictor: on average, TCNs who register with PES are 9.30 times (830%) more likely to get a job via PES The effect of mainstreaming on getting a job via PES: not significant Interaction effect (product term): significant for TCNs 243 Table A39 The Distribution of EU Residents per Country (LFS-2018) Country Three categories of EU residents Frequency Percent Host-country nationals 19,274 2.74 Austria Intra-EU migrants 1,851 0.26 Third-country nationals 1,466 0.21 Host-country nationals 38,594 5.48 Belgium Intra-EU migrants 3,501 0.50 Third-country nationals 1,460 0.21 Host-country nationals 5,460 0.78 Switzerland Intra-EU migrants 2,168 0.31 Third-country nationals 615 0.09 Host-country nationals 19,847 2.82 Germany Intra-EU migrants 1,062 0.15 Third-country nationals 1,502 0.21 Host-country nationals 12,671 1.80 Estonia Intra-EU migrants 95 0.01 Third-country nationals 1, 477 0.21 Host-country nationals 92,407 13.13 Spain Intra-EU migrants 1,712 0.24 Third-country nationals 2,918 0.41 Host-country nationals 12,840 1.82 Finland Intra-EU migrants 184 0.03 Third-country nationals 206 0.03 Host-country nationals 43,997 6.25 France Intra-EU migrants 900 0.13 Third-country nationals 2,558 0.36 Host-country nationals 57,304 8.14 Greece Intra-EU migrants 425 0.06 244 Country Three categories of EU residents Frequency Percent Third-country nationals 1,866 0.27 Host-country nationals 31,004 4.40 Ireland Intra-EU migrants 2,084 0.30 Third-country nationals 574 0.08 Host-country nationals 131,336 18.66 Italy Intra-EU migrants 3,322 0.47 Third-country nationals 7,792 1.11 Host-country nationals 3,995 0.57 Luxembourg Intra-EU migrants 2,373 0.34 Third-country nationals 339 0.05 Host-country nationals 31,664 4.50 Netherlands Intra-EU migrants 540 0.08 Third-country nationals 590 0.08 Host-country nationals 29,684 4.22 Poland Intra-EU migrants 14 0.00 Third-country nationals 88 0.01 Host-country nationals 37,412 5.31 Portugal Intra-EU migrants 219 0.03 Third-country nationals 472 0.07 Host-country nationals 9,533 1.35 Sweden Intra-EU migrants 213 0.03 Third-country nationals 211 0.03 Host-country nationals 75,551 10.73 United Kingdom Intra-EU migrants 3,984 0.57 Third-country nationals 2,578 0.37 245 Table A40. Education Levels of EU Residents (LFS-2018) Country Three categories of EU residents Education level Frequency Percent Low 2,525 0.41 Host-country nationals Medium 10,906 1.78 High 5,833 0.95 Low 256 0.04 Austria Intra-EU migrants Medium 940 0.15 High 655 0.11 Low 663 0.11 Third-country nationals Medium 520 0.09 High 283 0.05 Low 9,634 1.58 Host-country nationals Medium 11,932 1.95 High 10,081 1.65 Low 971 0.16 Belgium Intra-EU migrants Medium 921 0.15 High 1,064 0.17 Low 581 0.09 Third-country nationals Medium 310 0.05 High 307 0.05 Low 513 0.08 Host-country nationals Medium 2,794 0.46 High 2,147 0.35 Low 371 0.06 Switzerland Intra-EU migrants Medium 731 0.12 High 1,052 0.17 Low 187 0.03 Third-country nationals Medium 225 0.04 High 199 0.03 Low 2,181 0.36 Host-country nationals Medium 12,369 2.02 High 5,287 0.86 246 Country Three categories of EU residents Education level Frequency Percent Low 281 0.05 Germany Intra-EU migrants Medium 510 0.08 High 263 0.04 Low 665 0.11 Third-country nationals Medium 502 0.08 High 327 0.05 Low 1,910 0.31 Host-country nationals Medium 4,268 0.70 High 3,469 0.57 Low 10 0.00 Estonia Intra-EU migrants Medium 33 0.01 High 34 0.01 Low 214 0.03 Third-country nationals Medium 764 0.12 High 353 0.06 Low 40,540 6.63 Host-country nationals Medium 15,615 2.55 High 22,462 3.67 Low 484 0.08 Spain Intra-EU migrants Medium 538 0.09 High 444 0.07 Low 1,259 0.21 Third-country nationals Medium 641 0.10 High 449 0.07 Low 1,443 0.24 Host-country nationals Medium 6,056 0.99 High 5,341 0.87 Low 66 0.01 Finland Intra-EU migrants Medium 80 0.01 High 38 0.01 Low 75 0.01 Third-country nationals Medium 77 0.01 High 54 0.01 Low 9,037 1.48 247 Country Three categories of EU residents Education level Frequency Percent Host-country nationals Medium 20,622 3.37 High 14,010 2.29 Low 336 0.05 France Intra-EU migrants Medium 300 0.05 High 257 0.04 Low 1,403 0.23 Third-country nationals Medium 646 0.11 High 473 0.08 Low 24,549 4.01 Host-country nationals Medium 16,372 2.68 High 9,832 1.61 Low 132 0.02 Greece Intra-EU migrants Medium 185 0.03 High 81 0.01 Low 802 0.13 Third-country nationals Medium 599 0.10 High 114 0.02 Low 7,189 1.18 Host-country nationals Medium 8,323 1.36 High 8,012 1.31 Low 244 0.04 Ireland Intra-EU migrants Medium 683 0.11 High 813 0.13 Low 40 0.01 Third-country nationals Medium 130 0.02 High 288 0.05 Low 60,185 9.84 Host-country nationals Medium 39,713 6.49 High 15,415 2.52 Low 1,097 0.18 Italy Intra-EU migrants Medium 1,346 0.22 High 341 0.06 Low 3,855 0.63 Third-country nationals Medium 1,672 0.27 248 Country Three categories of EU residents Education level Frequency Percent High 642 0.10 Low 1,429 0.23 Host-country nationals Medium 1,462 0.24 High 960 0.16 Low 834 0.14 Luxembourg Intra-EU migrants Medium 512 0.08 High 922 0.15 Low 139 0.02 Third-country nationals Medium 58 0.01 High 108 0.02 Low 6,210 1.02 Host-country nationals Medium 13,100 2.14 High 12,089 1.98 Low 85 0.01 Netherlands Intra-EU migrants Medium 221 0.04 High 210 0.03 Low 196 0.03 Third-country nationals Medium 225 0.04 High 140 0.02 Low 3,014 0.49 Host-country nationals Medium 17,987 2.94 High 8,683 1.42 Low 1 0.00 Poland Intra-EU migrants Medium 7 0.00 High 6 0.00 Low 2 0.00 Third-country nationals Medium 40 0.01 High 46 0.01 Low 21,430 3.50 Host-country nationals Medium 6,155 1.01 High 5,163 0.84 Low 63 0.01 Portugal Intra-EU migrants Medium 71 0.01 High 72 0.01 249 Country Three categories of EU residents Education level Frequency Percent Low 185 0.03 Third-country nationals Medium 154 0.01 High 81 0.01 Low 1,082 0.18 Host-country nationals Medium 4,546 0.74 High 3,900 0.64 Low 32 0.01 Sweden Intra-EU migrants Medium 64 0.01 High 113 0.02 Low 102 0.02 Third-country nationals Medium 27 0.00 High 80 0.01 Low 12,397 2.03 Host-country nationals Medium 19,736 3.23 High 16,975 2.78 Low 528 0.09 United Kingdom Intra-EU migrants Medium 1,224 0.20 High 1,237 0.20 Low 443 0.07 Third-country nationals Medium 607 0.10 High 1,052 0.17 250 Table A41. Country-level Control Variables in the MLMS-LFS Sample (2018) Country CIVIX score Unemployment rate Foreign population in the EU GDP Welfare/ Expendure on social protection Austria 5.5 4.8 15.9 56,889 10, 965 Belgium 1.25 5.9 12.1 52,389 10, 044 Germany 7 3.4 12.9 54,457 11, 262 Spain 2.5 15.3 9.9 40,542 6, 233 Finland 2.5 7.4 4.5 49,543 10, 171 France 5 9.1 7.1 46,242 10, 916 Ireland 1 5.7 12.4 84,575 7, 153 Italy 4.25 10.6 8.7 42,798 8, 207 Netherlands 6.25 3.8 6.1 57,564 11, 015 Portugal 1.25 7 4.1 34,272 5, 607 Sweden 0 6.4 9 53,808 10, 219 United Kingdom 5.5 4 9 46,885 7, 991 251 Appendix B. Figures Figure B1. Cook’s Distance Measurements for Table A2. 𝐷* = 0.3 Figure B2. Cook’s Distance Measurements for Table A3. 𝐷* = 0.6 252 Figure B3. Cook’s Distance Measurements for Table A4. 𝐷* = 0.18
Thesis/Dissertation
eng
Political Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Ellermann, Antje, 1971-
Mainstreaming the labour market integration of immigrants in the EU : policy framework and policy impact
Text