The Economics of Immigration Beyond the Cities: Theoretical Perspectives and Empirical Insights 3031309677, 9783031309670

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Daniel Rauhut · Birgit Aigner-Walder Rahel M. Schomaker

The Economics of Immigration Beyond the Cities Theoretical Perspectives and Empirical Insights

The Economics of Immigration Beyond the Cities

Daniel Rauhut Birgit Aigner-Walder • Rahel M. Schomaker

The Economics of Immigration Beyond the Cities Theoretical Perspectives and Empirical Insights

Daniel Rauhut Centro de Estudos Geograficos Universidade de Lisboa Lisbon, Portugal

Birgit Aigner-Walder Carinthia University of Applied Sciences Villach, Austria

Rahel M. Schomaker Carinthia University of Applied Sciences Villach, Austria

ISBN 978-3-031-30967-0    ISBN 978-3-031-30968-7 (eBook) https://doi.org/10.1007/978-3-031-30968-7 © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The initiative for this book was triggered by the MATILDE project, a Horizon 2020 project coordinated by the University of Eastern Finland. The aim of the MATILDE project was to analyse the economic and social impacts of non-EU immigration to rural and mountainous regions. This volume brings together two different developments. First, it looks beyond the conventional categorisation of immigrants based on their cause of stay (labour migrants, tied movers, students, refugees, asylum-seekers, etc.), which is a categorisation that has difficulty in explaining if this new labour is demanded or not on the labour market, or to what extent it is allowed to work or not. Second, as economic structures differ geographically within countries, the labour market integration of the new labour added can be expected to display geographically different outcomes. Within the MATILDE project, this volume has a special function. While a major activity of the project was to collect data on the economic implications of non-EU immigration to rural and mountainous regions in selected case study regions, we have tried to identify the overarching key questions regarding the economic consequences of non-EU immigration to these areas beyond the cities. Notably, the economic effects of immigration to areas beyond major cities and metropolitan areas materialise differently than seen in the cities. There are several excellent books that explore the economics of immigration at a more technical and mathematical level, as well as many books targeting specialised academic disciplines. This volume is intended to target a wider audience including practitioners and stakeholders. It should be seen as an introductory non-technical book that lends an economic v

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PREFACE

perspective to the complex subjects of immigration, urban-rural differences, the functioning of the labour market and policy perspectives. As always, there are several people other than the authors involved in a book project. We want to thank Wyndham Hacket Pain for his help and support during the different stages in the production process, together with the Palgrave Macmillan production team. We also would like to thank our research assistant Christina Lobnig for her help, Nicholas Rowe for the language editing and the members of the MATILDE project for their interesting and inspiring discussions. Lastly, the financial support of the Horizon 2020 project ‘Migration Impact Assessment to Enhance Integration and Local Development in European Rural and Mountain Areas’ (MATILDE) under Grant Agreement no. 870831 is gratefully acknowledged. Lund, Sweden; Villach, Austria  January 2023 

Daniel Rauhut Birgit Aigner-Walder Rahel M. Schomaker

Contents

1 What  Is This Book About? Introductory Positioning  1 1.1 Trends in Migration Flows and Policies  1 1.2 Migration Flows and the Spatial Distribution of Immigrants  3 1.3 Economic Structure and Spatiality  5 1.4 The Economic Effects of Immigration  6 1.5 The Aim and Scope of This Book 10 1.6 Methodological Considerations and Personal Reflections 11 1.7 The Structure of the Book 12 References 13 2 Economic  Theory and Migration 21 2.1 Determinants of International Migration 21 2.2 Economic Effects of International Migration 24 Costs of Immigration  24 Benefits of Migration  26 Questioning the Assumptions  29 Other Aspects  31 2.3 Previous Empirical Results on the Effects of Immigration 33 Economic Consequences at an Aggregate Level  34 Economic Consequences at an Individual Level  40 2.4 Summary 43 References 44

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3 Migration  Beyond the Cities 51 3.1 Urban-Rural Divide 51 3.2 Attractiveness of Regions Beyond the Cities for Migrants 52 The Theoretical Perspective  52 Empirical Evidence on the Regional Preferences of Migrants  55 3.3 Effects of Migration on a Regional Level—Empirical Evidence 57 3.4 Analytical Framework on the Effects of Migration in Non-urban Areas 59 The Case of an Excess Demand for Labour  59 The Case of an Excess Supply for Labour  61 Other Aspects  65 3.5 Concluding Remarks and Guiding Research Questions 66 References 67 4 Data and Methods 71 4.1 Research Framework 71 4.2 Data 73 4.3 Indicators 74 4.4 Methods 81 4.5 Conclusion 81 References 81 5 Immigration  Beyond the Cities: An Analysis 83 5.1 Labour Market Needs in Rural Areas—An Ageing Europe 83 5.2 The Potential of Migrants—Educational Attainment Level and Employment Situation 90 5.3 Migrants as Consumers—The Economic Situation of Migrants 96 5.4 Migrants as Innovators?—A Way Out of a Potential Vicious Circle of Underdevelopment 99 5.5 Overall Economic Effects of Migration—TCN and Economic Growth104 5.6 Do Regions Beyond the Cities Gain Nevertheless?—The Relevance of Fiscal Regulations110 5.7 Summary114 References115

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6 Policy Considerations123 6.1 Existing Policies123 The Idea of an A-spatial Economic Structure 123 Immigrant Settlement Strategies 125 The Labour Market Situation for Immigrants 126 The Role of the European Union 129 6.2 The Needed Policy Design131 The Economic Heterogeneity of Immigrants 131 The Policy Actors 133 Policy Challenges to Address 135 6.3 Potential Ways Forward138 A New Classification for Migrants 138 A New System of Distribution of Refugees 140 The Chicken or the Egg: Which Came First? 141 6.4 Concluding Remarks142 References144 7 The  Multi-faceted Implications of Immigration: Reflections and Conclusions155 7.1 What Are the Main Findings?155 7.2 Policy Options157 7.3 Looking Onwards and Outwards160 7.4 Conclusion163 References165 Index167

About the Authors

Daniel  Rauhut holds a PhD in economic history and is Associate Professor of regional planning. Rauhut’s research focuses on migration, the integration of immigrants, welfare provision and the EU cohesion policy. He is a co-editor of the volumes Poverty in the History of Economic Thought (Routledge, 2020), Poverty in the Contemporary Economic Thought (Routledge, 2021), EU Cohesion Policy and Spatial Governance (Edward Elgar, 2021) and Assessing the Social Impact of Immigration in Europe: Renegotiating remoteness (Edward Elgar, 2023). He recently edited the volume New Methods and Theory on Immigrant Integration: Insights from Remote and Peripheral Areas (Edward Elgar, 2023). Birgit Aigner-Walder  holds a doctoral degree at University of Klagenfurt, Austria, is Professor of economics at the Carinthia University of Applied Sciences (CUAS) since 2014 and is head of the department for Demographic Change and Regional Development as well as part of the scientific management team of the Institute for Applied Research on Ageing since 2016. Her research focuses on demographic change and its economic consequences, migration, regional economics and public finance. She has worked for the Austrian economic research institutes IHS Kärnten and WIFO, and been part of several regional, national and international research projects. At the moment she is involved in research projects funded under the EU Horizon 2020, Active Assisted Living (AAL) and Interreg programme.

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Rahel M. Schomaker  holds a doctoral degree in economic policy from Münster University and a Habilitation with a venia legendi in economics and public administration from Speyer University. Currently, she is Professor of economics and public administration at the Carinthia University of Applied Sciences, an adjunct professor at the German University of Administrative Sciences Speyer as well as a senior fellow at the German Research Institute for Public Administration. Her research focuses on administrative change, crisis governance, institutional economics, migration and trust. She is involved in several research projects funded under the EU Horizon 2020 programme; her work has appeared in a variety of international journals in economics, political science and public administration, and she serves as a senior advisor for the OECD, the EU and different national governments.

List of Figures

Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4 Fig. 5.5

Effect of immigration on wages. (Source: Borjas (1999a, p. 1701), adapted) 25 Immigration surplus. (Source: Borjas (1995a), adapted) 26 Immigration surplus in the presence of external effects. (Source: Borjas (1995a), adapted) 28 Gain from complete immigration in a two-region economy. (Source: Borjas (2001a), adapted) 33 Labour supply under an inelastic labour demand. (Source: Own compilation) 60 Migration in the case of an excess demand for labour. (Source: Own elaboration) 62 Migration and excess supply for labour. (Source: Own compilation)63 The vicious circle of regional underdevelopment. (Source: Modified after Capello, 2016, p. 104) 64 Share of TCN in and beyond the cities. (Source: Own compilation)86 Share of population 15–64 years old in metropolitan and non-­metropolitan regions (2019). (Source: Own compilation) 87 GDP per capita at NUTS 2 regions by share in tertiary education for all regions. (Source: Own elaboration) 88 The share of population with tertiary education by type of territory. (Source: Own elaboration) 92 Primary income by type of territory. (Source: Own elaboration) 99

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List of Figures

Fig. 5.6 Fig. 5.7 Fig. 5.8 Fig. 5.9 Fig. 5.10 Fig. 5.11 Fig. 5.12 Fig. 5.13 Fig. 5.14

Company start-ups and share of non-EU28 population in non-­metropolitan regions. (Source: Own elaboration) GDP in PPS per inhabitant by regional category. (Source: Own elaboration) Annual percentage change of gross value added (GVA). (Source: Own elaboration) Unemployment rate by share of non-EU28 population with primary education in non-metropolitan regions. (Source: Own elaboration) Employment rate by share of non-EU28 population with primary education in non-metropolitan regions. (Source: Own elaboration) Economic growth and the share of non-EU28 population with tertiary education in metropolitan regions. (Source: Own elaboration) Economic growth and the share of non-EU28 population with primary education in metropolitan regions. (Source: Own elaboration) Fiscal effects in theory. (Source: Own elaboration) Age-specific net contributions to the Danish public finances by origin in 2018 (thousands DKK). (Source: Danish Ministry of Finance (2018))

102 105 107 107 108 109 109 111 112

List of Tables

Table 3.1 Overview of existing theories on the determinants of international migration for different spatial areas Table 4.1 Overview of variables Table 5.1 Population projection, EU countries, 2020–2050 Table 5.2 Source for population development, EU countries, 2020 Table 5.3 Educational attainment levels for the studied areas and countries in 2019 (%) Table 5.4 Unemployment by country of birth 2009 and 2019 (%) Table 5.5 Median equivalised net incomes (€) 2009 and 2019 in the analysed EU countries Table 5.6 At-risk-of-poverty rates during 2009 and 2019 in the analysed EU countries (%) Table 5.7 Net business population growth in 2018 Table 5.8 GDP per capita and real growth rates in national economic centres and rural, remote, peripheral and mountainous regions studied in selected countries (2019) Table 6.1 Alternative analytical dimensions

53 75 84 85 91 93 97 98 101 106 139

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CHAPTER 1

What Is This Book About? Introductory Positioning

1.1   Trends in Migration Flows and Policies For decades, migration has influenced the economies and societies of Europe and the European Union. The number of labour migrants increased in Europe until 2019, and for 2020 the number of labour migrants dropped due to the Covid-19 pandemic. Family related migration dropped by ca. 30% between 2019 and 2020. Despite a similar drop of ca. 30% in the number of asylum applications, asylum seeking also remained at high levels during the pandemic (OECD, 2021). Trend wise, immigration to Europe has increased over time and it is not expected to decrease. The share of asylum-seekers from countries outside Europe constitutes a significant share of the migrants headed for Europe. In 2017, 3.1 million first resident permits to non-EU citizens were issued by EU Member States, and approximately half of them were granted due to work or studies (Hofmann, 2020). Countries such as France, the UK, Italy, Spain, Germany, but also Portugal and Sweden, attract much of the immigration flows (all types of immigrants) and will probably do so for quite some time (McAuliffe & Triandafyllidou, 2021). The numbers of refugees, asylum-­ seekers, internally displaced and stateless persons have more than doubled globally between 2011 and 2021 from 42.7 million to 89.3 million, and not since the end of World War II have these numbers been so high (UNHCR, 2022). Although many politicians, practitioners and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Rauhut et al., The Economics of Immigration Beyond the Cities, https://doi.org/10.1007/978-3-031-30968-7_1

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stakeholders hope for a quick fix on how refugees become integrated into the host community, historical experiences suggest that this process can take quite some time. One example is that the last refugee camp in Germany after World War II was not closed until 1960 (Rauhut, 2014). Over the last 30 years, Europe has experienced several major refugee flows, with a major impact on most Member States. The Yugoslavian civil war in the early 1990s generated the biggest refugee flow in Europe since World War II, with refugees spread across Europe (de Haas et al., 2016). The 2015 ‘refugee crisis’ generated another major refugee flow. Its origin is multifaceted. One issue deals with the inability to uphold and control EU external borders, and a second relates to the fact that Turkey decided to stop preventing refugees from crossing the border into the EU. This was a protest against the EU closing the door on Turkey becoming an EU candidate country. In this sense, the refugee crisis was predominantly a political crisis (Laine, 2020). Thirdly, the recent refugee flow from Ukraine has caused 7.8 million Ukrainians to seek refuge in EU countries, with a further 6.9 million Ukrainians displaced in Ukraine. Although the Russian war on Ukraine has not come to an end, the number of refugees to the EU is already twice as high as the number of refugees in Europe after World War II, but these numbers of refugees, asylum-seekers and displaced persons are already obsolete (Rauhut et al., 2022). Contrasting the trends and eventual turning points in migration to the European Union against the immigration policies of the EU Member States, immigration policies appear to have taken impression of these developments. STEM-workers (science, technology, engineering, mathematics) and other highly skilled labour are attracted in most countries. Many countries apply separate visa rules for this kind of labour, and as English is the lingua franca within these branches, STEM-workers are highly mobile (Chiswick, 2019; Hogarth, 2021). To attract highly international and mobile labour, not only the construction of visa rules is an important aspect. Tax rules (e.g., Portugal—see Silva, 2022) and welfare services (e.g., Sweden—Swedish Ministry of Foreign Affairs, 2005) are examples of tools that have been employed to attract this labour. In many cases, international students who have studied science, technology, engineering and mathematics are welcome to stay in the country of study after graduation (e.g., the US—Chiswick, 2019), which is another tool used to attract this demanded labour. Over the last few decades, immigration policy has changed for these types of potential immigrants. The applied changes aim at simplifying

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immigration for highly attractive labour and are a response to the high demand for this kind of labour (Hogarth, 2021; Rothwell & Ruiz, 2013). Asylum-seekers and refugees have also experienced changes in the immigration policies, but in the opposite direction. In most cases, countries have made it more difficult for asylum-seekers and refugees to enter a country (del Guercio, 2019), and it is felt that the return rate of asylum-­ seekers who have had their applications rejected and of irregular migrants should be increased (Ataç & Schütze, 2020). This is a parallel development to the increasing global trend in refugees (UNHCR, 2022; OECD, 2021). For tied movers and family reunification, the ability to be provided with appropriate housing and subsistence by family already residing in the country is an important consideration to be granted a visa (European Union, 2003).1 Consequently, family reunification for low-income households is difficult as they have difficulties in meeting the economic requirements for obtaining a visa in many countries (MIPEX, 2020).

1.2   Migration Flows and the Spatial Distribution of Immigrants Europe and the countries within the European Union have experienced significant changes in international migration trends over the last decades. Some scholars argue that the period between 1950 to the mid-1970s is characterised by labour migration and migration flows related to decolonisation, the period between the mid-1970s to the end of 1980s is characterised by the oil crisis and migration control, while the migration flows since the 1990s are dominated by refugees (Van Mol & de Valk, 2013). During the period up to the 1990s, low-skilled labour was headed for the big cities, as the prospects of finding a low-productive and labour-­intensive job were highest in the cities. At the same time, the metropolitan areas attracted the well-educated labour, and most countries also experienced an urbanisation process during this period, which meant that native labour left rural areas in favour of urban areas (Vandermotten et al., 2004). To large extent, these demographic changes reflected a changing economic geography of Europe (Champion et al., 1996). 1  Most EU Member States and European Free Trade Association (EFTA) States are subject to the EU’s Family Reunification Directive. However, Denmark, Iceland, Ireland, Norway, Switzerland and the UK permitted family reunification for refugees along similar lines, but with extra requirements (UNHCR, 2015).

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However, refugees have constituted a steady migration flow to the Western Europe ever since World War II. Immediately after the war, the refugees were of German and East European origin, and the East European refugees came in a steady flow until the collapse of the Communist system in the late 1980s. Most of the non-European immigration originated in the former colonies of countries such as the UK, France, Spain, Portugal, the Netherlands and Belgium (Fassmann & Münz, 1994). Besides the significance of East-to-West migration within Europe (Fassmann, 2009), South-to-North migration flows were also significant (de Haas et  al., 2016). Since the late 1960s and early 1970s, major global refugee flows originating in the Middle East, Africa and Latin America have reached Europe. The main refugee flows during the 2010s also originated from these regions (de Haas et al., 2016). Since the late 1980s, attempts have been made to limit the extra-European immigration to West European countries (Van der Kaa, 1993; de Haas et al., 2016). In the wake of the financial crises in the EU Member States during 2008/2009, a new wave of migration from southern Europe to northern Europe emerged (Lafleur & Stanek, 2017). This is a story of migration flows between countries. However, the immigrants were not evenly distributed within the countries of destination. In many countries, refugees were used as an instrument to create a counter-­ urbanisation in the 1980s to mitigate the rural exodus that had been seen from the 1960s and 1970s (Champion et al., 1996). In some countries, refugees were only allowed to settle down outside cities after World War II until the end of the 1950s (Rauhut, 2014). Today, the high share of asylum-­ seekers in rural regions in countries such as Austria, Finland, Germany, Italy, Norway and Sweden are a result of dispersal policies that distribute asylum-seekers purposefully or due to existing federal re-­ allocation schemes to rural and mountain areas (Weidinger, 2018). In most countries, refugees and asylum-seekers are accommodated in peripheral areas during the asylum process (Proietti & Veneri, 2019). The availability of accommodation is an aspect that is considered when resettling refugees in peripheral locations (Tardis, 2019; Gauci, 2020). Consequently, small labour markets with a limited demand for labour will suddenly host an excess supply of labour.

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1.3  Economic Structure and Spatiality Several prominent economists (e.g., Adam Smith, Alfred Marshall and Alfred Weber) studied the relationship between cities and urbanisation, on the one hand, and economic activity and innovation, on the other, since the late eighteenth century until the early twentieth century. So did the German geographers Walter Christaller and August Lösch in the 1930s (Rauhut & Hatti, 2017). In the 1980s, a renewed interest in the causality between cities and economic growth emerged. As the urban environment stimulates economic diversification and a cross-pollination between people and various economic activities, cities were considered to be an important driver behind economic growth. Jacobs (1984) termed these entities as urbanisation economies. The theoretical argument was that the ability to share resources, quicker and better matching, more learning, and innovative activities within close distances were seen as the drivers of higher productivity, and employment and economic growth were more advanced in cities (Duranton, 2014). The New Theory of Economic Growth and the New Economic Geography emerged in the 1980s, focusing on the endogenous factors of economic growth. Following the argument put forward by these theories, cities played an important role in economic growth (Romer, 1986, 1994; Lucas, 1988; Krugman, 1991a, 1993). During the 1990s and early 2000s, scholars such as Porter (1990), Castells (1996), Sassen (1991), Florida (2002) and Dickens (2003), to mention a few, argue for the central role that cities play in economic growth, but from different perspectives and scientific disciplines. Foreign direct investments (hereafter FDIs) as well as bigger domestic investments usually go to areas located close to the market, with good access to available labour of the correct type, and good opportunities for quick returns on investment. Hence, cities and urban agglomerations are favoured, and peripheral and remote areas are disfavoured (Tewdwr-Jones & Morais Mourato, 2005). This is especially so when talking about the post-industrial society, globalisation and the ICT revolution. Within the European Union about 80% of the territory is classified as predominantly rural, and the population residing in rural areas represents about 30% of the overall population. Most of the rural areas have a GDP per capita significantly below the EU average, and many are among the least favoured regions in the EU (CEC, 2021)—being what Rodríguez-­ Pose (2018) calls ‘the places that don’t matter’. Many rural regions,

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especially in southern Europe, have suffered from economic stagnation since the financial crisis in 2008 (Rauhut & Costa, 2021a; CEC, 2022). A similar development is to be found in rural and peripheral Finnish and Swedish regions due to extremely sparsely populated areas, and the deindustrialisation and decline in the primary sector have not been counterbalanced by an expanding market demand in the service sector (Rauhut & Costa, 2021b). OECD (2018) argues that structural shifts in manufacturing and natural resource-­based industries combined with population loss and ageing have caused some rural communities to be left behind, something which has fuelled discontent. Accordingly, the capacity of governments to effectively address these challenges and generate opportunities to turn the development around is crucial (OECD, 2018). The Rural Agenda of the European Union (CEC, 2021) aims to help rural regions grow and raise employment and living standards by improving the competitiveness of agriculture, achieving a sustainable management of natural resources and climate action, and a balanced territorial development of rural areas. Despite the rather massive empirical evidence that the economic conditions, economic activities and economic structure differ between different types of territory, the economic potential of peripheral, remote, mountainous and rural regions is recurringly overestimated and is based on the belief that economic activities are a-spatial. When seen from this perspective, however, these regions constitute an underutilised economic resource (Membretti et  al., 2017; Perlik & Membretti, 2018) and immigration will help to revitalise these regions economically and mitigate the problems associated with population ageing (Perlik et  al., 2019; Rye & O’Reilly, 2020; Przytuła & Sułkowski, 2020). However, these claims have been questioned in other studies (Poot, 2008; Gaspar et al., 2005; Coppel et al., 2001), and the OECD, 2021emphasises that immigration can both mitigate and aggravate these problems, but not solve them.

1.4   The Economic Effects of Immigration Few topics have been debated as much as the economic consequences of immigration. The topic is highly ideological and causes much contention. The findings of such debates suggest results that point in all kinds of directions, depending on what assumptions and premises the analyses are built on. Based on neoclassical economic theory, immigration is always economically beneficial for the host country (Simon, 1998), which is the same

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position advocated by the currently popular economic theory of the New Left, Modern Monetary Theory (MMT), (Hansen, 2021; Kelton, 2020).2 While Simon (1998) represents a libertarian view on the effects of immigration, the MMT represents the opposite: as long as countries control their own currency, they can ignore budget deficits and use the money for refugee reception. As refugees are also consumers, this will stimulate the economy and hence refugee reception will be beneficial for all (Hansen, 2021; Kelton, 2020).3 On the other hand, the OECD concludes that immigration may—given a set of assumptions—mitigate some of the labour shortage that an ageing population causes, but it cannot solve the budgetary implications of ageing populations (Coppel et  al., 2001; Bodvarsson & Van den Berg, 2013). The more favourable results of the economic consequences are based on the premise that restrictions are raised against low-skilled immigrants (Kondoh, 2017). While labour immigrants have a job waiting for them upon arrival, far from all refugees find a job and are hence dependent on welfare transfers. In many countries this is a costly consequence of immigration (Bodvarsson & Van den Berg, 2013; Bansak et al., 2021), and although there is a huge variation between different countries, immigrant integration programmes and integration policies consume significant resources in the host countries (Doomernik & Bruquetas-Callejo, 2016). In the economic literature, the economic consequences of immigration are seen as a function of issues such as the immigrants’ human capital, language proficiency, welfare use, economic mobility and impact on the labour market in the host country (Borjas, 2001, 2019; Chiswick, 2019). The most significant economic consequences of immigration are found in labour market and fiscal issues (Mayr, 2012). Moreover, there are also differences in the economic impact of permanent and temporary migrant workers (Dustmann & Görlach, 2016), which highlights the need to bear in mind that immigrants are a heterogeneous group. Furthermore, not all immigrants migrate due to economic incentives. Bodvarsson and van den Berg (2013, p. 305) point at a key issue when observing that: 2  The heterodox economic theory of MMT is based on the assumption that the government can produce a just provision of social welfare by raising taxes and printing more money. Its critics argue that it is a political ideology rather than a concise and coherent economic theory (Drumetz & Pfister, 2021a, 2021b). 3  Unfortunately, no country controls its currency 100%, which makes MMT unrealistic in its premises.

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a group of international migrants that do not fit the standard model of immigration very well are asylum seekers and refugees. These are people who have left their home countries to escape actual or feared political, religious or social persecution, or other threats to their safety and well-being. They are not so much responding to the economic pull of higher incomes overseas as they are being pushed or driven out of their native countries by war or intolerable political and social conditions. (italics in original)

Refugees differ from labour migrants, and so does their labour market success relative to labour migrants in the new host country (Hatton, 2013). The resettlement of refugees within the host countries is usually arbitrary (Bansak et al., 2021), and in many countries refugees are mechanically resettled to peripheral regions to level out population imbalances, adding labour to regions struggling with declining populations (Golebiowska et al., 2011). As refugees have not been selected economically in the same way that labour immigrants have, their labour market situation is fundamentally different than of labour immigrants. Brell et al. (2020, p.  94) conclude that ‘refugees typically arrive in a host country with less locally applicable human capital, including language and job skills, than economic migrants and consequently are likely to start at significantly lower levels of wages and employability’. To put refugees into work is a challenge (OECD, 2016), and because of a marginal position on the labour market, many immigrants, both labour immigrants and refugees, become self-employed (Stark, 1991). As delineated, economic activities are not evenly spread across a country, nor is the demographic structure and its socio-economic profile identical in all regions and municipalities. But immigrants are not usually evenly distributed in the host country. Hence, it can be assumed that the economic consequences of immigration will vary depending both on the characteristics of the regional/local economic structure and on the socio-­ economic profile of the immigrants themselves. Notwithstanding this, an overwhelming majority of all the studies on the economic consequences of immigration focus on the national level (e.g., Brell et  al., 2020; Peri, 2016; Kondoh, 2017; Borjas, 2019; Chiswick, 2019; Bodvarsson & Van den Berg, 2013; Bansak et  al., 2021; Nijkamp et  al., 2012; Artal-Tur et al., 2014). Some studies have been carried out regarding the economic impact of immigration to cities. This literature is, however, very focused on the US (Ottaviano & Peri, 2013; Lewis & Peri, 2014; Card, 2007; Kemeny, 2014). It is not a bold assumption to make that the two extreme

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examples of cities and peripheral/remote places in Europe have completely different possibilities for utilising the potential that immigration can bring. Nor can we assume that the findings from the US cities are readily transferable to European cities and even less to peripheral, remote, rural and mountainous regions. But hitherto, this is largely unexplored in economic research. Over the last few years, an increasing amount of literature has emerged on how immigrants in general, and especially refugees, can economically revitalise rural, remote, peripheral and mountainous regions. This literature has some distinct characteristics. First, the studies are done by sociologists, anthropologists, ethnographers, geographers, spatial planners and political scientists (e.g., Perlik et  al., 2019; Rye & O’Reilly, 2020; Przytuła & Sułkowski, 2020; Hansen, 2021; ESPON, 2019), although a few economists have also studied this issue (Erol & Unal, 2022, Brell et  al., 2020; Bansak et  al., 2021; Bodvarsson & Van den Berg, 2013). Second, most of the studies arguing that refugees will revitalise these regions are based on the logic of the lifestyle migration theory, in which well-off persons with significant economic and non-economic resources move to these regions to enjoy self-perceived well-being and quality of life (Kordel et  al., 2018; Kordel & Weidinger, 2020; Perlik et  al., 2019). Originally, the lifestyle migration theory has been related to retirement migration, seasonal migration and residential tourism (O’Reilly, 2001; Benson, 2011; Benson & O’Reilly, 2009; Benson & Osbaldiston, 2014; Rodríguez, 2001, Torkington et  al., 2015). But to us, it seems a bold assumption to use such theory when analysing the economic consequences of refugee immigration to rural areas, and it remains to be seen to what extent this bold assumption can stand up to empirical test. Third, most of the studies arguing that immigration can economically revitalise rural, remote, peripheral and mountainous regions are based only on qualitative methods and data (Perlik et al., 2019; Galera et al., 2018; Bianchi et al., 2021; Kordel & Weidinger, 2020; ESPON, 2019). Fourth, this literature does not always distinguish between different types of immigration, for example refugees and labour migrants (Kordel & Weidinger, 2018; Rye & O’Reilly, 2020; Galera et  al., 2018; Bianchi et  al., 2021), and as noted above, different types of immigrants will perform differently. Fifth, no distinction is made between EU citizens and extra-EU citizens (e.g., Galera et al., 2018; Rye & O’Reilly, 2020; Kordel & Weidinger, 2018), which is a remarkable shortcoming, especially as the legal status of the two different groups is fundamentally different (e.g., Mügge & van der Haar,

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2016). In other words, in the current literature, an intra-EU labour migration of EU citizens is analysed in the same way as for refugees from outside the EU. In an analysis on the economic consequences of immigration, these aspects must be handled in a different way. For the time being, the number of economic studies on the economic consequences of immigration at sub-national levels are few, and it is generally considered difficult to place refugees into the migration logic based on labour migrants. However, in the wider social science field, many studies on the economic consequences of immigration have been made by non-economists. The strength of these studies is that they approach the question at sub-national levels, but on the downside, fundamental aspects such as the different types of immigrants and at what level the consequences materialise are blurred. Moreover, intra-EU labour migrants are lumped together with extra-EU refugees, which creates a significant degree of bias when analysing the economic consequences of immigration (see also Dustmann et al., 2016).

1.5   The Aim and Scope of This Book This volume discusses what economic consequences immigration will have on rural, peripheral, remote and mountainous regions—that is places beyond the cities. As the economic consequences usually are discussed at a national level, but most consequences materialise at sub-national levels, we want to highlight a certain type of territory. Economic activities are not a-aspatial, and consequently, neither are the economic consequences of immigration. The analysis will be carried out in two ways: (1) We undertake quantitative analyses based on Eurostat data for all regions in the European Union, and the findings are compared and contrasted against previous research findings. (2) We zoom into a set of EU countries (Austria, Bulgaria, Finland, Germany, Italy, Spain and Sweden) in places beyond the cities (i.e., non-metropolitan regions). A key question in this study is who an immigrant is. According to the Maastricht Treaty 1992 (European Union, 1992), EU citizens have the same civic rights as natives in another EU country with the exception that they are not allowed to vote in the national parliament. Hence, an EU citizen is to be considered as a native in another EU Member State and is not subject to any integration policies in the EU country in which they reside (Mügge & van der Haar, 2016). Moreover, the free mobility of labour and the integration of the EU labour market are to a larger extent driven

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by political ambitions to deepen the integration of the EU’s inner market than on actual labour demands (Zaiceva & Zimmermann, 2008; Straubhaar & Wolter, 1997).4 Consequently, we only focus on non-EU citizens when we talk about immigrants. Within our study, the term third country nationals (hereafter TCN) is also used for non-EU citizens. In this study we focus on areas ‘beyond the cities’, which means that we define what is a city and also what is beyond the cities. Instead of following the urban-rural typology offered by Dijkstra and Poelmann (2008), we use the metropolitan/non-metropolitan regions typology offered by Eurostat (2023). In short, in this typology, metropolitan regions are approximations of functional urban areas of 250,000 or more inhabitants including the commuting zones. Several types of metro-regions can be distinguished: (1) the capital metro-region includes the national capital; (2) second-tier metro-regions are regions including the largest cities in a country, excluding the capital; (3) smaller metro-regions can have a relatively low population and be placed far away from the capital and the second-­tier metro-regions. Consequently, the smaller-tier metro-regions are not considered as metro-regions due to their population size. The regions which do not belong to a metro-region are simply considered non-metro-regions.

1.6   Methodological Considerations and Personal Reflections The empirical material consists of both qualitative and quantitative data. The qualitative data originates from the different deliverables from the Horizon 2020 funded project ‘Matilde’.5 This part of the data consists of interviews and focus groups of stakeholders (public sector, private sector and third sector), TCN who have immigrated to regions beyond the cities in the EU and experts in the related fields.6 It also consists of qualitative 4  This argument is developed further in Straubhaar (1988, 2001). With the exception of a few countries, the relative unwillingness to move to another EU country supports the argument that the free trade of goods will make free movement of labour less important. 5  For details on the project, see https://matilde-migration.eu. 6  For an overview, see the project reports by, for example, Bianchi et al. (2021), Aigner-­ Walder et  al. (2021), Laine and Rauhut (2021), Laine (2021), Caputo et  al. (2021), Membretti (2022), Gilli and Membretti (2022).

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assessments of statistical data (Aigner-Walder et al., 2021; Laine & Rauhut, 2021). The quantitative data used for the analysis is mainly based on Eurostat data, to guarantee a comparability between countries and regions. Migrants are not identical. The qualifications of migrants are different, and hence they will perform differently in the labour market. Moreover, depending on their legal status, not all of them are (immediately) allowed to work. Additionally, the location of their settlement matters, and while some regions might urgently need labour, migrants will cause an excess supply of labour in others. Economic activities are not space neural, nor is the distribution of migrants. Furthermore, the economic consequences of migration are not uniform throughout the economy. We can expect to see macroeconomic consequences (e.g., in the form of tax revenues, net transfers and social spending) and company-related consequences (e.g., labour supply, structural change and competitiveness), as well as consequences at an individual level (e.g., unemployment, welfare dependency and wage levels).

1.7   The Structure of the Book This book contains seven chapters. Chapter 2 discusses the economic effects of migration in general. The existing theoretical approaches as well as previous empirical results are presented and discussed within this chapter. In Chap. 3 we discuss the effects of migration beyond the cities and outline a theoretical framework to explain what happens on the labour market in the case of an excess supply or an excess demand of labour. Methodological considerations and data are reflected on in Chap. 4. In Chap. 5 we discuss the empirical economic consequences in cities and beyond the cities both qualitatively and quantitatively. The focus is on the regional level (NUTS 2) in the EU, with a specific consideration of seven selected countries and additional case study regions. How policies can be designed to optimise the economic consequences of immigration by different types of immigrants is discussed and contrasted against the current policy design in Chap. 6. Chapter 7 discusses the findings of the study, what can be learnt from it and what the added value is, and outlines avenues for future research.

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References Aigner-Walder, B., Luger, A., & Schomaker, R. M., Eds. (2021). Economic impact of migration: Statistical briefings. MATILDE deliverable 4.2. https://doi. org/10.5281/zenodo.4817376 Artal-Tur, A., Peri, G., & Requena-Silvente, F. (Eds.). (2014). The socio-economic impact of migration flows. Springer. Ataç, I., & Schütze, T. (2020). Crackdown or symbolism? An analysis of post-2015 policy responses towards rejected asylum seekers in Austria. In S. Spencer & A.  Triandafyllidou (Eds.), Migrants with irregular status in Europe (pp. 117–137). Springer. Bansak, C., Simpson, N., & Zavodny, M. (2021). The economics of immigration. Routledge. Benson, M. (2011). The British in rural France. Lifestyle migration and the ongoing quest for a better way of life. Manchester University Press. Benson, M., & O’Reilly, K. (Eds.). (2009). Lifestyle migration. Expectations, aspirations and experiences. Ashgate. Benson, M., & Osbaldiston, N. (Eds.). (2014). Understanding lifestyle migration: Theoretical approaches to migration and the quest for a better way of life. Palgrave Macmillan. Bianchi, M., Caputo, M. L., Lo Cascio, M., & Baglioni, S. (2021). A comparative analysis of the migration phenomenon: A cross-country qualitative analysis of the 10 country reports on migrants’ economic impact in the MATILDE Regions. Deliverable 4.4. https://doi.org/10.5281/zenodo.5017818 Bodvarsson, O.  B., & Van den Berg, H. (2013). The economics of immigration. Theory and policy. Springer. Borjas, G. J. (2001). Heaven’s door. Immigration policy and the American economy. Princeton University Press. Borjas, G. J. (2019). Reflections on immigration economics. In B. Elsner (Ed.), Foundations of migration economics (pp. 573–582). Oxford University Press. Brell, C., Dustmann, C., & Preston, I. (2020). The labour market integration of refugee migrants in high-income countries. Journal of Economic Perspectives, 34(1), 94–121. https://doi.org/10.1257/jep.34.1.94 Caputo, M.  L., Bianchi, M., Membretti, A., & Baglioni, S., Eds. (2021). Ten country reports on economic impact. MATILDE deliverable 4.3. https://doi. org/10.5281/zenodo.5017813 Card, D. (2007). How immigration affects U.S. cities. CReAM Discussion Paper Series No. 11/07. University College London. Retrieved June 16, 2022, from https://www.cream-­migration.org/publ_uploads/CDP_11_07.pdf Castells, M. (1996). The rise of the network society. The information age: Economy, Society and Culture. Vol. 1. Blackwell.

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CEC. (2021). A long-term vision for the EU’s rural areas—Towards stronger, connected, resilient and prosperous rural areas by 2040. COM(2021) 345 final. CEC (2022). Cohesion in Europe towards 2050. Eighth report on economic, social and territorial cohesion. Publications Office of the European Union. Champion, T., Mønnesland, J., & Vandermotten, C. (1996). The new regional map of Europe. Progress in Planning, 46, 1–89. Chiswick, B.  R. (2019). Managing immigration in the 21st century. In B.  Elsner (Ed.), Foundations of migration economics (pp. 583–595). Oxford University Press. Coppel, J., Dumont, J. C., & Visco, I. (2001). Trends in immigration and economic consequences. Economics department. Working Paper series No. 284. OECD. de Haas, H., Castles, S., & Miller, M.  J. (2016). The age of migration. Guildford Press. del Guercio, A. (2019). Cross-border movement and human rights within the framework of European Asylum Policy. In M. Perlik, G. Galera, I. Machold, & A.  Membretti (Eds.), Alpine refugees. Immigration at the core of Europe (pp. 18–27). Cambridge Scholars Publishing. Dicken, P. (2003). The global shift. London: Paul Chapman Publishing. Dijkstra, L., & Poelmann, H. (2008). Remote rural regions. How proximity to a city influences the performance of rural regions. Regional Focus No. 01/2008. Retrieved September 26, 2022, from https://ec.europa.eu/regional_policy/ sources/docgener/focus/2008_01_rural.pdf Doomernik, J., & Bruquetas-Callejo, M. (2016). National immigration and integration policies in Europe since 1973. In B. Garcés-Mascareñas & R. Penninx (Eds.), Integration processes and policies in Europe (pp. 57–76). Springer. Drumetz, F., & Pfister, C. (2021a). Modern monetary theory: A wrong compass for decision-making. Intereconomics, 56(6), 355–361. Drumetz, F., & Pfister, C. (2021b). The meaning of MMT. Banque de France Working Paper, 833. Retrieved June 6, 2022, from https://publications. banque-­france.fr/sites/default/files/medias/documents/wp833_0.pdf Dustmann, C., & Görlach, J. S. (2016). The economic of temporary migrations. Journal of Economic Literature, 54(1), 98–136. https://doi.org/10.1257/ jel.54.1.98 Dustmann, C., Schönberg, U., & Stuhler, J. (2016). The impact of immigration: Why do studies reach such different results? Journal of Economic Perspectives, 30(4), 31–56. https://doi.org/10.1257/jep.30.4.31 Duranton, G. (2014). Growing through cities in developing countries. World Bank Research Observer, Policy Working Paper No. 6818. Erol, I., & Unal, U. (2022). Employment effects of immigration to Germany in the period of migration policy liberalization, 2005–2018. Eurasian Economic Review, 12, 531–565. https://doi.org/10.1007/s40822-­022-­00199-­4 ESPON. (2019). MIGRARE—Impacts of refugee flows to territorial development in Europe. ESPON.  Retrieved June 12, 2022, from https://www.espon.eu/ sites/default/files/attachments/MIGRARE_Final_Report.pdf

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European Union. (1992). The Treaty of the European Union / Treaty of Maastricht. Signed in Maastricht, the Netherlands, 7 February 1992. Official Journal of the European Communities (92/C 191 /01 ). European Union. (2003). COUNCIL DIRECTIVE 2003/86/EC of 22 September 2003 on the right to family reunification. Eurostat. (2023). Territorial typologies for European cities and metropolitan regions. Retrieved January 3, 2023, from https://ec.europa.eu/eurostat/statistics-­ explained/index.php?title=Territorial_typologies_for_European_cities_ and_metropolitan_regions#A_typology_of_metro.28politan.29_regions Fassmann, H. (2009). European migration: Historical overview and statistical problems. In H. Fassmann, U. Reeger, & W. Sievers (Eds.), Statistics and reality. Concepts and measurements of migration in Europe (pp. 21–44). Amsterdam University Press. Fassmann, H., & Münz, R. (1994). Patterns and trends of international migration in Western Europe. In H. Fassmann & R. Münz (Eds.), European migration in late twentieth century (pp. 3–33). Edward Elgar. Florida, R. (2002). The rise of the creative class. Basic Books. Galera, G., Giannetto, L., Membretti, A., & Noya, A. (2018). Integration of migrants, refugees and asylum seekers in remote areas with declining populations. OECD Local Economic and Employment Development (LEED) Working Papers 2018/3. https://doi.org/10.1787/20794797 Gaspar, J., Marques da Costa, N., d’Abreu, D., Marques da Costa, E., Barroqueiro, M., Estevens, A. & Rauhut, D. (2005). Ageing, Labour Shortage and ‘Replacement Migration’, Centro de Estudos Geográficos, Universidade de Lisboa. Gauci, J.  P. (2020). Integration of migrants in middle and small cities in rural areas in Europe. European Committee of the Regions. Commission for Citizenship, Governance, Institutional and External Affairs. https://doi. org/10.2863/281960 Gilli, M., & Membretti, A., Eds. (2022). 13 action-research reports. MATILDE deliverable 5.3. https://doi.org/10.5281/zenodo.6372113 Golebiowska, K., Valenta, M., & Carter, T. (2011). International immigration— Trends and data. In D.  Carson, R.  O. Rasmussen, P.  Ensign, L.  Huskey, & A. Taylor (Eds.), Demography at the edge. Remote human populations in developed nations (pp. 53–84). Ashgate. Hansen, P. (2021). A modern migration theory. An alternative economic approach to failed EU Policy. Agenda Publishing. Hatton, T. (2013). Refugee and asylum migration. In A.  F. Constant & K. F. Zimmermann (Eds.), International handbook on the economics of migration (pp. 453–469). Edward Elgar. Hofmann, M. (2020). Labour migration—Five priorities for the EU and its member states. International Centre for Migration Policy Development. Retrieved January 19, 2023, from https://www.icmpd.org/news/labour-­migration-­five­priorities-­for-­the-­eu-­and-­its-­member-­states

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Hogarth, T. (2021). COVID-19 and the demand for labour and skills in Europe: Early evidence and implications for migration policy. Migration Policy Institute Europe. Jacobs, J. (1984) Cities and the wealth of nations. Principles of economic life. Vintage. Kelton, S. (2020). The deficit myth: Modern monetary theory and the birth of the people’s economy. Public Affairs. Kemeny, T. (2014). Immigrant diversity and economic performance in cities. International Regional Science Review, 40(2), 164–208. https://doi.org/ 10.1177/0160017614541695 Kondoh, K. (2017). The economics of international immigration. Springer. Kordel, S., Weidinger, T., & Jelen, I., Eds. (2018). Processes of immigration in rural Europe: The status quo, implications and development strategies. Newcastle upon Tyne: Cambridge Scholars. . Kordel, S., & Weidinger, T. (2020). Migration processes in European rural and mountainous areas. In S. Kordel & A. Membretti (Eds.), Classification on spatial specificities and third country nationals distribution in Matilde regions (pp. 31–47). Deliverable 2.1. Retrieved June 9, 2022, from https://matilde-­ m i g r a t i o n . e u / w p -­c o n t e n t / u p l o a d s / 2 0 2 0 / 0 8 / M AT I L D E _ D 2 1 _ Classification_on_spatial_specificities_and_TCNs_distribution_040820.pdf Krugman, P. (1991a). Increasing returns and economic geography. Journal of Political Economy 99(3), 483–499. Krugman, P. (1993). First nature, second nature and the metropolitan location. Journal of Regional Science, 33(2), 129–144. Lafleur, J. M., & Stanek, M. (2017). Lessons from the South-North migration of EU citizens in times of crisis. In J. M. Lafleur & M. Stanek (Eds.), South-North migration of EU citizens in times of crisis (pp. 215–224). Springer. Laine, J. P. (2020). Safe European home—Where did you go? On immigration, the b/ordered self, and the territorial home. In J.  P. Laine, I.  Moyo, & C. Changwe Nshimbi (Eds.), Expanding boundaries. Borders, mobilities and the future of Europe-Africa relations (pp. 216–236). Routledge. Laine, J., Ed. (2021). Ten country reports on qualitative impacts of TCNs. MATILDE deliverable 3.3. https://doi.org/10.5281/zenodo.4726645 Laine, J., & Rauhut, D., Eds. (2021). Ten statistical briefings on immigration’s social impacts. MATILDE deliverable 3.2. https://doi.org/10.5281/zenodo. 4726634 Lewis, E., & Peri, G. (2014). Immigration and the economy of cities and regions. In J.  G. Duranton, V.  Henderson, & W.  C. Strange (Eds.), Handbook of regional and urban economics (pp. 625–685). Elsevier. Lucas, R. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42.

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Mayr, K. (2012). Die ökonomischen Auswirkungen von internationaler Migration. In H. Fassmann & J. Dahlvik (Eds.), Migrations- und Integrationsforschung— multidisziplinäre Perspektiven (pp. 109–122). V&R Unipress. McAuliffe, M., & Triandafyllidou, A. (Eds.). (2021). World migration report 2022. International Organization for Migration (IOM). Membretti, A., Ed. (2022). 13 quantitative briefing on the case studies. MATILDE deliverable 5.2. https://doi.org/10.5281/zenodo.5526040 Membretti, A., Kofler, I., & Viazzo, P.  P., Eds. (2017). Per forza o per scelta: L’immigrazione straniera nelle Alpi e negli Appennini. Aracne et al. MIPEX. (2020). Family reunion. Retrieved July 15, 2022, from https://www. mipex.eu/family-­reunion Mügge, L., & van der Haar, M. (2016). Who is an immigrant and who requires integration? Categorizing in European policies. In B.  Garcés-Mascareñas & R. Penninx (Eds.), Integration processes and policies in Europe (pp. 77–90). Springer. Nijkamp, P., Poot, J., & Sahin, M. (Eds.). (2012). Migration impact assessment: New horizons. Edward Elgar. O’Reilly, K. (2001). The British on The Costa Del Sol. Routledge. OECD. (2016). Making integration work. Refugees and others in need of protection. OECD Publishing. https://doi.org/10.1787/9789264251236-­en OECD. (2018). Policy note rural 3.0. A framework for rural development. OECD. OECD. (2021). International migration outlook 2021. OECD. Ottaviano, G., & Peri, G. (2013). New frontiers in immigration research: Cities and firms. Journal of Regional Science, 53(1), 1–7. https://doi.org/10.1111/ jors.12011 Peri, G. (Ed.). (2016). The economics of international migration. World Scientific. Perlik, M., & Membretti, A. (2018). Migration by necessity and by force to mountain areas: An opportunity for social innovation. Mountain Research and Development, 38(3), 250–264. Perlik, M., Galera, G., Machold, I., & Membretti, A., eds. (2019). Alpine refugees. Immigration at the core of Europe. Cambridge Scholars. Poot, Jacques (2008): Demographic change and regional competitiveness. The effects of immigration and ageing. International Journal of Foresight and Innovation Policy, 4(1/2), 129–145. Porter, M. (1990). The competitive advantage of nations. Free Press. Proietti, P., & Veneri, P. (2019). The location of hosted asylum seekers in OECD regions and cities. Journal of Refugee Studies, 34(1), 1243–1268. https://doi. org/10.1093/jrs/fez001 Przytuła, S., & Sułkowski, L. (Eds.). (2020). Integration of migrants into the labour market in Europe: National, organizational and individual perspectives. Emerald. Rauhut, D. (2014). I moder Sveas ömma famn. Carlssons förlag.

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Rauhut, D., & Costa, N. (2021a). What regions benefit from the post-crisis Cohesion Policy? Evidence from a Territorial Cohesion Development Index. In D. Rauhut, F. Sielker, & A. Humer (Eds.), The EU’s cohesion policy and spatial governance: Territorial, economic and social challenges (pp. 185–198). Edward Elgar. Rauhut, D., & Costa, N. (2021b). Territorial cohesion in Denmark, Finland, Norway and Sweden: Measuring the impact 2007 and 2017. Danish Journal of Geography, 121(1), 1–14. https://doi.org/10.1080/00167223.2021.1920444 Rauhut, D. & Hatti, N. (2017) Cities and Economic Growth: A Review. Social Science Spectrum 6(1): 1–15 Rauhut, D., Velasco Echeverría, X., Komornicki, T., Czapiewski, K., et al. (2022). Policy brief: Russian invasion of Ukraine, the refugees flows and possible implications for Cohesion Policy. ESPON. Rodríguez, V. (2001). Tourism as a recruiting post for retirement migration. Tourism Geographies, 3(1), 52–63. Rodríguez-Pose, A. (2018). The revenge of the places that don’t matter (and what to do about it). Cambridge Journal of Regions, Economy and Society, 11(1), 189–209. https://doi.org/10.1093/cjres/rsx024 Romer, P. (1986). Increasing Returns and Long-Run Growth. Journal of Political Economy, 94(5), 1002–1037. Romer, P. (1994). The origins of endogenous growth. Journal of Economic Perspectives, 8(1), 3–22. Rothwell, J. T., & Ruiz, N. (2013). H-1B visas and the STEM shortage: A research brief. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2262872 Rye, F.  J., & O’Reilly, K. (Eds.). (2020). International labour migration to Europe’s rural regions. Routledge. Sassen, S. (1991). The global city. New York: Princeton University Press. Silva, L. (2022). Guide to the non-habitual resident (NHR) tax regime. Retrieved July 15, 2022, from https://www.portugal.com/moving-­to-­portugal/guide-­ to-­the-­non-­habitual-­resident-­nhr-­tax-­regime/ Simon, J.  L. (1998). The economic consequences of immigration. University of Michigan Press. Stark, O. (1991). The migration of labor. Blackwell. Straubhaar, T. (1988). Internal labour migration within a common market: Some aspects of EC experience. Journal of Common Market Studies, 27(1), 45–62. Straubhaar, T. (2001). East-West Migration: Will it be a problem? Intereconomics, 36(4), 167–170. Straubhaar, T., & Wolter, A. (1997). Globalisation, internal labour markets and the migration of the highly skilled. Intereconomics, 32(4), 174–180. Swedish Ministry of Foreign Affairs. (2005). Images of Sweden abroad. Fritzes. Tardis, M. (2019). Another Story from the “Refugee Crisis”. Resettlement in Small Towns and Rural Areas in France. Études de l’Ifri. Accessed on 28 January 2023, from https://www.ifri.org/en/publications/etudes-de-lifri/ another-story-refugee-crisis-resettlement-small-towns-and-rural-areas

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Tewdwr-Jones, M., & Morais Mourato, J. (2005). Territorial cohesion, economic growth and the desire for European ‘balanced competitiveness. Town Planning Review, 76(1), 69–80. Torkington, K., David, I., & Sardinha, J. (Eds.). (2015). Practising the good life: Lifestyle migration in practices. Cambridge Scholars Publishing. UNHCR. (2015). Family reunification in Europe. Retrieved July 15, 2022, from https://www.unhcr.org/56fa38fb6.pdf UNHCR. (2022). Global trends report in forced displacement in 2021. Statistics and Demographics Section, UNHCR Global Data Service. Van der Kaa, D. J. (1993). European migration at the end of history. European Review, 1(1), 87–108. Van Mol, C., & de Valk, H. (2013). Migration and immigrants in Europe: A historical and demographic perspective. In B. Garcés-Mascareñas & R. Penninx (Eds.), Integration processes and policies in Europe (pp. 31–56). Springer. Vandermotten, C., Van Hamme, G., Medina Lockart, P., & Wayens, B. (2004). Migrations in Europe. The four last decades. Società Geografica Italiana. Weidinger, T. (2018). Residential mobility of refugees in rural areas of Southeastern Germany. Structural contexts as influencing factors. In S. Kordel et al. (Eds.), Processes of immigration in rural Europe: The status quo, implications and development strategies (pp. 178–202). Zaiceva, A., & Zimmermann, K. F. (2008). Scale, diversity, and determinants of labour migration in Europe. Oxford Review of Economic Policy, 24(3), 427–451.

CHAPTER 2

Economic Theory and Migration

2.1   Determinants of International Migration The determinants of international migration are extensively discussed in theory, although under differing perspectives and assumptions. While some analyse the decision to migrate in terms of individuals or households, others consider migration flows between countries or examine institutional aspects of relevance. There are also various existing sources which present and discuss different concepts and theories (e.g., Massey et  al., 1993; Schoorl, 1995). From a micro perspective, an individual or a household who is considering an out-migration might incorporate various aspects when making a decision. Lee (1966) distinguished between factors that push individuals or households away from their home country (e.g., poverty, low wages), and pull factors such as personal freedom or high wages that make the receiving country attractive. Conversely, stay factors within the host country (e.g., family ties or property) as well as ‘stay away’ factors of the receiving country (e.g., language or cultural barriers) could hinder an out-migration. Moreover, the costs of moving (e.g., transportation costs or travel time) as well as potential formal barriers to migration (e.g., entry visa) have to be considered (see Bodvarsson & van den Berg, 2013 for a graphical depiction of various relevant factors). In this context, the difference between refugees, labour migrants and other migrants has to be scrutinised, as the single theories presented below have a different explanatory power for different forms of migration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Rauhut et al., The Economics of Immigration Beyond the Cities, https://doi.org/10.1007/978-3-031-30968-7_2

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According to the neoclassical migration theory, individuals are rational actors who aim to maximise their income in the long run. Consequently, costs and potential risks as well as the expected wage level are considered as relevant factors in taking a migration decision (Sjaastad, 1962; Todaro, 1969, 1976; Borjas, 1990). Transferred to a macro perspective, migration is a consequence of differences in the labour markets in different countries, leading to a long-run equilibrium of labour markets (see Sect. 2.3). Accordingly, this implies that there would be no migration between countries if no wage differential exists (Ranis & Fei, 1961; Harris & Todaro, 1970).1 The assumptions of the neoclassical model are criticised by the new economics of migration insofar that decisions are not taken on an individual level, but the societal dimension is of relevance (Stark, 1978, 1991; Stark & Bloom, 1985). A household has the potential to spread risks by allocating its resources, and where different sources of income could come from different countries. A stable income from a receiving country would offer security in the case of a deterioration of the income in the home country, especially as there are low insurance and credit possibilities existing in developing countries. This implies that wage differentials are not absolutely necessary for migration. The labour market is also focused in the dual or segmented labour market theory (Piore, 1979; Morokvasic, 1984; Kofman, 1999), which states that the demand for workers in developed countries is a pulling factor for international migration. The domestic supply of low-level workers is limited, often due to the unstable conditions and low prestige of these jobs, and raising the wage as an incentive would lead to a more expensive structural inflation,2 which makes migrants attractive. The consequence would be a segmented labour market, with migrants employed in low-paid jobs and worse working conditions than natives. The migration systems or networks theory states that over time, migration systems are evolving. For example, trade or cultural relationships between countries can motivate stable migration flows/international migration systems between the country of origin and the host country (Mabogunje, 1970). The formation of networks in the receiving countries makes 1  In classical economics, W. Arthur Lewis (1954) and Adam Smith (Rauhut, 2010) consider wage differences and unemployment as drivers of migration. 2  To raise the wage of low-level jobs would have implications for the total hierarchy (acceptance, status), with the need for a proportional increase of all wages, which is known as structural inflation.

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migration even more attractive, as it lowers risks and may ease the access to labour markets. The consequence is a self-reinforcing process, where the existing networks motivate people to migrate, which leads to an even bigger network and incentive in the future. The world systems theory (Wallerstein, 1974) considers international migration more globally, and peripheral developing countries are of interest for capital owners in order to raise their profits (e.g., by way of existing natural resources, new consumer markets or access to cheap labour). As a consequence, productivity in agriculture increases, leading to a decrease in demand for manual/low-wage labour. In the search for new opportunities, these workers could migrate, and migration to the origin countries of capital is easier due to existing links (e.g., transportation, communication). This makes migration more likely between colonially connected countries. The theory of cumulative causation (Myrdal, 1957; Massey, 1990) describes migration as a dynamic process. Families who receive remittances achieve higher incomes, making other families feel relatively deprived. This further motivates people to migrate, creating even more income inequality in the country of origin. Having these higher incomes, migrants buy land which is withdrawn from production due to the low productivity when compared to working abroad. With less jobs in agriculture, out-migration increases again. Hence, a culture of migration evolves. But migrants are also needed within the receiving countries, as the jobs they take over are labelled as migrant jobs, being less attractive for natives. A different perspective is proffered by institutional theory (Massey et  al., 1993), as the relevance of institutions for migration is in focus. There are institutions present that encourage migration, and a market for migration exists. For example, smugglers, transportation businesses, agencies, and so on, all make a profit out of migration and ease entering the labour market for migrants, making other sources of migration less relevant. Moreover, humanitarian organisations fight for the rights and assistance of migrants. Finally, mobility transition theory (Zelinsky, 1971) considers demographic and societal transition processes. First, within developing countries it expects migration from rural to urban areas in a period of strong demographic growth. Secondly, migration to developed countries follows. As the demographic growth in advanced economies shrinks, an urban-to-­ urban mobility follows. Moreover, the need to import low-skilled labour leads to a circular migration.

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2.2  Economic Effects of International Migration After discussing the question of the motives for migration based on the economic theories of the determinants of migration, the second relevant question is one focusing on the effects of migration. Here, we focus on the economic effects of migration in the receiving country. The elementary model for a discussion of the economic effects of migration is the basic labour market model. Due to migration, we would expect an increase in total income in the receiving country. With the move of migrants to a new country and their shift in consumption expenditures, a consequence of a higher demand for labour may also be seen. Wages in the receiving country might fall due to a higher supply of labour. In the long run, dynamic growth effects are also expected, as immigrants are an additional source of innovation or they might be active as entrepreneurs, so economies of scale could be used (Bodvarsson & van den Berg, 2013, p. 22f). But the presented effects are not that clear-cut, and any assumptions have to be discussed in detail. Costs of Immigration Decrease in Wages In regard to wages, we follow the argument put forward by Borjas (1999a). We assume a simple model of an economy whose production depends on capital and labour, with a workforce consisting of native and immigrant workers. Moreover, we assume that all workers are perfect substitutes and that capital is owned only by natives. Additionally, labour and capital are inelastic, and due to constant returns of the production function, the output of the economy is fully distributed to workers and capital owners. Figure 2.1 presents the effects of immigration on wages. MPL is the marginal product of labour. Without immigration/with natives (N) only on the labour market, the wage would be at the level w0. The entry of immigrant workers (M) on the labour market shifts the supply curve to the right, as more workers are available (L=N+M). Consequently, the market wage is lowered to w1. Hence, this basic labour market model implies a decrease in wages in the receiving country due to a higher competition in the labour market, based on the well-known and simple supply and demand principle (for details, see Pindyck & Rubinfeld, 2017 or Mankiw & Taylor, 2020). As

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Fig. 2.1  Effect of immigration on wages. (Source: Borjas (1999a, p.  1701), adapted)

migrants enter the market and supply their labour, prices fall. Due to the shift in labour supply, the real wage of competing native workers is lowered. But, as we will see later on, the ‘natives on the whole gain substantially from immigration’ (Borjas, 1999a, p. 1702), and the assumptions of this model are very unrealistic. Costs of Integration A second aspect to be considered as costs of immigration (and which is often much more present in public debates than the effects on wages) is the public costs of integration. For the integration of migrants, one would most likely expect increased costs in the fields of education or social welfare programmes. But besides the costs of services for migrants, the taxes paid by migrants also have to be considered (Borjas, 2019; Chiswick, 2019). Johnson (1980) argues that the immigration of low-skilled migrants could have negative effects (also for high-skilled migrants and capital owners) if it leads to the unemployment of low-skilled natives who then depend on unemployment compensation. Hence, in the case of a progressive tax system, those with higher incomes as capital owners or high-skilled workers will bear the majority of the burden in regard to the higher fiscal costs.

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Immigration may also increase ‘congestion in public services such as schools, roads, sewers, electric power systems and parks (…) crime, pollution, and the destruction of traditional culture’ (Bodvarsson & van den Berg, 2013, p. 182). Subsequently, xenophobia can also be considered as an externality to immigration (Layard et al., 1992), making aspirations for integration even more important. Benefits of Migration  he Immigration Surplus T Besides the described costs, there are also benefits from migration that may be expected. Borjas (1995a) argues that under a competitive framework (market clearance and full employment), according to welfare economics and free trade principles, a movement of factors of production should increase welfare and efficiency. First, we again consider an aggregate production function with the two inputs of capital and labour. Secondly, we once more assume that capital is solely owned by native workers and that immigrants and natives are perfect substitutes, as well as a perfectly inelastic supply of capital. Figure 2.2 shows the effects of immigration on total output. MPL is the marginal product of labour, and the area under the curve presents the

Fig. 2.2  Immigration surplus. (Source: Borjas (1995a), adapted)

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economy’s output. Hence, without immigration, the national income to natives (N) is the area ABN0. As soon as immigrants enter the labour market, the supply curse shifts to the right with the consequence of a lower market wage and a higher national income which is now presented by the area ACL0. A part of the income increase is distributed to immigrants as they receive income for their work. The part of the income increase which goes to natives is shown under BCD. Borjas argues that an immigration surplus only arises in the case of a falling wage for natives. The losses by the lower wage are more than offset by an increase of income of capitalists, as rental prices for capital increase. If the wages of natives hardly react to immigration, the surplus would be close to zero. In the case of a great reduction in wage, the native population in total would gain in welfare. But, Borjas also highlights with a calculation for the US, that the immigration surplus would be very small (0.1% of GDP). Although the overall economic effects would be rather small, immigration would lead to a fairly large redistribution of wealth from labour to capital. Additionally, it has to be considered that the total immigration surplus depends on the costs expended for the integration of immigrants. I ncrease in Market Size As Borjas (1995a, p.  11) argues, ‘Immigration expands the size of the market.’ The expansion of the market also leads to a higher level of competition, which might result in productivity increases. Additionally, new exchanges between workers and firms might lead to new knowledge, again resulting in increasing returns. Growing markets may also enable the exploitation of economies of scale and a stimulation of specialisation (Bodvarsson & van den Berg, 2013). The shifting of the marginal product of the labour curve from MPL to MP´L can increase the size of the immigration surplus substantially. In the following figure, the change in the national income of natives is shown by the sum of the triangle BCD and the trapezoid ABEF (Fig. 2.3). Natives could also profit from lower product prices due to lower labour costs induced by migration. These potential effects are subsumed as demand effects of immigration (Bodvarsson & van den Berg, 2013, p. 125ff). But Borjas (1995a) also questions the relevance of these external effects of immigration and argues that an increase in congestion as well as decreasing returns to scale might be consequences of immigration, as other production factors remain fixed.

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Fig. 2.3  Immigration surplus in the presence of external effects. (Source: Borjas (1995a), adapted)

I ncrease in Local Consumption One additional aspect subsumed under the demand effect is an increase in (local) demand due to immigration. The higher number of people leads to more consumption. Hence, positive economic effects are expected in the receiving country. Immigrants consume as soon as they arrive (e.g., food, transportation, housing), with or without them having a job. As wages fall, while native workers would not gain from the ‘immigration surplus’, mainly migrants and capital owners would profit, which, as mentioned before, would lead to an income redistribution from workers to capital owners. But the negative effects on workers could be reduced if migrants consume the local output. Especially, a demand for goods which are not tradeable (i.e., many services) would lead to an increased product demand where workers are located. Hence, migration also leads to a shift in the demands of consumers. These demand shifts stimulate the demand for labour and increase the product supply from which native consumers also benefit (see Bodvarsson & van den Berg, 2013, p. 124ff for a detailed discussion on the effects on consumption). The extent of the effect depends on the propensity of migrants to consume compared to natives, as well as their possibilities to find a job. The stimulation of demand may lead to an increase in product prices, but

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production costs and domestic prices may also decrease due to a higher supply of labour. Which of these price effects dominate depends on elasticities of wages and prices, the participation rate of migrants in the goods and labour market, as well as the output shares of labour and capital. Moreover, the rate of migrants’ remittances and whether the migration movements are demand-pulled or supply pushed play a role, with more positive effects to be expected in the case of demand-pulled migration. In general, the negative effects on earnings of both natives and immigrants or the out-migration of natives would reduce demand and so cause a fall in product price, while the effect of more consumers would increase prices. Hence, the effect on wages depends on the substitution of natives by immigrants, with a negative effect on wages, on the one hand, and potential positive effects on wages due to more local consumer demand, on the other. Importantly, the demand effect could also be the reason why only a low effect of immigration has been seen on wages in destination countries (Bodvarsson & van den Berg, 2013, p. 127ff). Questioning the Assumptions The delineated developments are based on strict assumptions, wherein the model assumes fixed capital which is owned by natives and a perfect substitution between natives and immigrants (for a detailed description, see Bodvarsson & van den Berg, 2013, p. 110ff). However, these assumptions are rather unrealistic, and consequently, different variations are now discussed.  o Fixed Capital N First of all, in the case of variable capital there might be no immigration surplus. If immigrants bring in capital, the immigration surplus for natives might be smaller or even zero, as immigrants get the returns for their product (Borjas, 1995a). Moreover, a high return to capital could motivate inflows of capital from other countries. This would increase the demand for labour with the consequence of raising wages. The preliminary negative effects of immigration on workers would be reduced. However, the inflow of capital would stop as soon as the return for capital reaches world level, so leaving wages at the level present before immigration. The described development would only take place in small countries which do not influence the overall capital market on the world and also in the case of a perfect elastic capital supply. But as capital is not perfectly

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elastic, an immigration surplus is to be expected. Additionally, this implies that the immigration surplus might only be temporary in small countries, and the gains to immigrants are greater if capital flows flexibly between countries (Bodvarsson & van den Berg, 2013).  o Perfect Substitution N The assumption of a perfect substitution between immigrants and natives is unrealistic. Differentials in skills exist within the native as well as the immigrant populations. The type of migration inflow in the sense of the composition of skills effects the size of the immigration surplus. For a simple case, we assume only two types of skills—skilled and unskilled labour, a perfectly inelastic labour supply, no external effects and ignore the role of capital in production. If the skill composition of immigrants is equal to that of natives, then the wages of both skilled and unskilled workers would be unaffected by immigration (under the condition of a production function with constant returns to scale), with the consequence of no welfare gains being seen from immigration. If the skill composition of immigrants is different compared to those of natives, the highest immigration surplus can be generated under the circumstance of a complete differentiation in skills. For example, if the native population is relatively skilled, the immigration of unskilled people would maximise the immigration surplus (Borjas, 1995a). From a distributional point of view, if immigrants were more skilled, the wages of more skilled natives would fall and those of the unskilled increase. Consequently, besides capital owners, those natives that are not present within the group of migrants will be seen to gain in wage (Bodvarsson & van den Berg, 2013, p. 118ff). Taking into account the role of capital within the economy and assuming that it is solely owned by natives, we again see an immigration surplus as long as the immigration of skilled workers leads to a reduction in wages for skilled persons, as well as if the immigration of unskilled workers reduces the wages of unskilled natives. Borjas (1995a) argues that the immigration surplus may be larger if skilled workers immigrate, as their wages respond more extensively to shifts in supply. The greater immigration surplus can partly also lead back to the existence of production complementarities between skilled labour and capital. Moreover, it can be expected that the costs of unskilled migration are larger, as unskilled migrants are more likely to use additional services, as well as pay lower taxes.

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But, within this overall discussion, it has to be highlighted that even within a certain skill group, perfect substitution is not to be expected as differences in language, education or culture might be of relevance (for a discussion, see Ottaviano & Peri, 2005). Other Aspects  echnical Progress and Product Mix T Acemoglu (1998) points out that the structure of the labour force affects the market for technologies. A high proportion of skilled labour would lead to technologies which are complementary. Hence, immigration could even affect the use of technology, insofar that a higher amount of low-­ skilled workers could slow down automatisation processes, or vice versa, automatisation could be increased in the case of the immigration of primarily high-skilled workers. According to the Rybczynski theorem of international trade, also the sector which profits from the reduction of the wages of unskilled workers will increase returns, with the consequence of an expansion, and a higher demand and wage for unskilled labour. Wages could return to the previous level, but the product mix within the region would be changed. Hence, immigration could also lead to a differing product mix (for a discussion of existing literature, see Bodvarsson & van den Berg, 2013, p. 123f). Growth models focusing on technical progress ‘see people as the resource that thinks of new ideas, develops new technologies, and applies new methods and procedures’ (Bodvarsson & van den Berg, 2013, p. 231). Hence, immigrants can be seen as a resource for innovation, be it as entrepreneur or worker. Moreover, immigration could be a driver for technological progress, to overcome diminishing returns. Additionally, technology transfer between countries could be simplified due to existing communication channels and favourable individual characteristics (such as language skills, cultural knowledge, etc.) (Bodvarsson & van den Berg, 2013, p. 231ff). I ncrease of Internal Migration There is the further question of whether migration really has a negative effect on the wages of natives. The local labour market cannot be seen as closed market, as potentially the free movement of labour, capital and goods might well equalise factor prices. If native workers or firms are

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moving to other localities with better opportunities, then the effects of immigration on wages might be low (Borjas, 1995a). In an additional article, Borjas (2006) focuses on the internal migration flows of natives as a consequence of migration. The fall of wages as a consequence of immigration could lead to an out-migration of natives to other regions. This would set off labour market effects of migration in the long-term. Moreover, the capital of natives could be moved to other localities until returns to capital are again equal between regions. This would also happen without migration, in the case of excess demand, supply of workers or capital. Borjas argues that as a consequence of these adaptation processes, regional analyses of the economic effects of migration might come up with little effects. However, this does not mean that immigration had no effect on the (regional) economy. According to this argumentation, one would expect that regions with a high inflow of migrants would experience a comparable high out-migration of natives, and in regions with a low level of migrants, an in-migration of natives would be high. These adaptations would reduce the effects of migration on wages in regions. But a presumption of these adaptations furnishes perfect information of workers in the context of regional wage differences (Borjas, 1999b).  ecrease of Regional Wage Differentials D Regional differences in wages should lead to labour flows between regions which would improve labour market efficiency. Borjas (2001a) argues that even in the US, labour mobility is not sufficient to get rid of regional differentials in wages. Immigrants are likely to cluster in regions where wages are higher and could subsequently support equalising wages and opportunities throughout a country. Native workers might fail to utilise regional wage differentials, especially in the case of high migration costs (e.g., leaving family and friends behind). As a consequence, their marginal product is lower than it could be, in the case of a movement to a region with higher economic perspectives. On the other hand, migrants have already taken the decision to accept the costs of migration, and the costs for choosing another region or state within the country might be only a little higher. Hence, for migrants, the chance is higher that they will arrive in regions with the highest wages. In that way, immigration speeds up the regional convergence in wages, with the effect of an improved economic efficiency. The following figures show the gains of immigration in a multi-region economy, with higher wages in region 1 in the initial equilibrium. N is the

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Fig. 2.4  Gain from complete immigration in a two-region economy. (Source: Borjas (2001a), adapted)

total number of native labour, with n1 allocated in region 1 and n2 allocated in Region 2, together summing up to N. We again assume that labour is supplied inelastically in both of the regions and that the whole (fixed) capital is owned by natives. Due to differentials in supply, the wage in region 1 (w1) is higher than the wage in region 2. We assume an immobility of natives. In the case that immigration perfectly balances out the supply differential between region 1 and region 2 (all migrants enter region 1), then immigration would lead to an equalisation of wages. The area between points B, C and E illustrates an increase in GDP of the country due to the adaptation processes (Fig. 2.4). For further discussions on the theoretical framework (e.g., on the effects of an imperfect location decision of migrants or of differing labour demand curves in the regions) see Borjas (2001a).

2.3   Previous Empirical Results on the Effects of Immigration In this subchapter we survey the empirical evidence of the economic consequences of immigration. The empirical literature is extensive. However, the studies focus on narrow themes and have little ‘systemic’ perspective. Generally, the literature focuses on the US and on Europe. The focus is often on the economic consequences at an aggregate level. Lastly, the empirical evidence indicates that there are many types of economic consequences for the host country.

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Economic Consequences at an Aggregate Level Economic Growth Based on the previous theoretical discussions on the effects of immigration on the economy of the destination country, we would expect positive effects of immigration on economic output. While the increase in physical capital might at an individual level have negative effects on wages, it could stimulate growth at a macro level. Moreover, an increase of human capital could lead to innovation and technical progress, which according to new growth models are seen as the main sources for long-term economic growth. For the new EU members (accession from 2004 onwards), Sardadvar and Vakulenko (2021) show that the human capital in regions with an already positive net-migration increases once net-migration rates go up. But focusing on the EU27 from 1995 to 2007, Huber et al. (2010) found mixed results. A study by Boubtane et al. (2016) firstly reveals a positive impact of migrant human capital on GDP per capita, and secondly that a permanent increase in migration flows has a positive effect on productivity growth and on GDP per capita, and a negative impact on the aggregated unemployment, and native- and foreign-born unemployment rates. However, the growth impact of immigration is small even in countries that have highly selective migration policies. A prospective study by Treyz and Evangelakis (2018) models the scenario of net migration to the US, finding that in the absence of immigration, total US employment would peak in 2019, and the US GDP and labour force would decline by 20% through 2060. Other studies find positive effects of migration, and Hofer and Weyerstraß (2016) found a positive impact on GDP growth for Austria, with migration providing the opportunity to counteract the negative economic consequences of ageing. D’Albis et al. (2019) were able to show that the macroeconomic and fiscal consequences of international migration are positive for OECD countries, as it produces a demographic dividend by increasing the share of the workforce within the population. Thus, the migration shock increases GDP per capita through a positive effect on both the ratio of working age to total population and the employment rate. Moreover, it improves the fiscal balance by reducing the governmental per capita transfers paid. If immigrants have lower human capital than the native population, then economic growth will slow down. But economic growth will increase

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if immigrants have a higher human capital than the native population (Friedberg & Hunt, 1995; Borjas, 1995a).3 At a given point in time, the continued immigration of low-skilled labour leads to reduced growth as the proportion of low-productivity work increases (Stark & Yitzhaki, 1982). While immigration may cause an increase in economic growth on a short-term perspective, the long-term effects are contradictory. Ekberg (1977) uses empirical data from the 1950s and onwards to estimate the long-term effects on economic growth by immigration in Sweden. The findings suggest that labour immigration has no long-term effect on economic growth; however, the short-term effects are significant. Simon (1998) simulates the impact of immigration on the US economy, and his findings indicate significant long-term effects on economic growth. Another estimation of the impact on economic growth by immigrants is made by Borjas (2001b), where he finds only short-term effects. According to Borjas (2001b), while there is no doubt immigrants contribute to economic growth, this is not to any dramatic numbers. Three important cautions must be made regarding the completely different findings that are mentioned here: (1) While Ekberg (1977) uses empirical data and analyses the impact of immigration on economic growth historically up to his contemporary time, Simon (1998) simulates the effects. (2) While the US economy is big and to a large extent protected, the Swedish economy is small and open. (3) Calculations are highly dependent on the assumptions made, something that both Borjas (2001b) and Storesletten (2002) recognise. Ismail and Yuliyusman (2014) focus on the impact of foreign labour on economic growth and show that skilled and semi-skilled foreign labour has a positive and significant impact on the output growth in both the 3  It is not only in neoclassical economics this is argued. Similar reasoning exists in the already mentioned Marxist Dual Labour Market Theory. Industrialisation and economic development have meant that many jobs have been rationalised away and that the general level of education of the labour force has been raised. Simultaneously, a more unskilled workforce is often still needed for certain types of work. When there is a shortage of labour in the lower segment of the labour market, this causes economic growth to slow down. The problem can mostly be solved by replacing work with capital. In labour-intensive industries in the lower segment of the labour market, labour cannot be replaced by capital when the native labour force rejects these industries. The alternatives are either to import labour or for society to undergo a more radical change in society and its institutions, work organisation, and technology. Growth can be sustained in the short-term through labour immigration, while a radical change in society has positive effects on long-term growth (Piore, 1979).

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short and long run, and that unskilled foreign labour adversely affects output growth in both the short and long run. It is worth remembering that this discussion does not distinguish between labour immigrants and refugees, nor does it consider any geographical differentiations. Rather, these findings imply that immigrants are a homogeneous group and that economic growth is spread evenly across a country. While many studies focus on the more developed countries of the world (as these countries attract most of the inward migration of labour force), the effects of migration differ depending on the development level of a county. Bove and Elia (2017) investigated the extent to which cultural diversity affects economic growth and whether this relation depends on the level of development of a country. They found that migration and diversity have a distinct positive impact on real GDP per capita, with the effect of diversity being more consistent in developing countries. Focusing on the national level, scrutinising the heterogeneity of immigrants, host countries’ income and productivity, Aleksynska and Tritah (2015) found that immigrants have a positive effect on income that works primarily through total factor productivity (TFP). Contrasting income effects exist across age groups: in detail, a higher share of immigrants among the youth has a negative impact on aggregate income, while a higher share of immigrants among prime-aged workers has a positive effect. Poot (2008) provides empirical evidence that population ageing reduces regional competitiveness, while immigration (particularly of entrepreneurs and highly skilled workers to metropolitan areas) enhances competitiveness. However, while the immigration of young individuals can slow down ageing, the levels needed to stabilise the trend are not realistic, and ageing may even subsequently accelerate. A study of the Netherlands by Muysken and Ziesemer (2014) demonstrates that permanent shocks in immigration will help to alleviate the ageing problem in the long run, as long as the immigrants will be able to participate in the labour force at least as much as the native population. The better educated the immigrants are or become, the higher their contribution to growth will be. Even temporary immigration may help to alleviate the ageing challenge through a positive long-term contribution to employment, wages and GDP per capita. Docquier et al. (2015) calculate the effect of a complete liberalisation of cross-border migration on the world GDP and its distribution across regions and find that liberalising migration increases the world GDP by

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11.5–12.5% in the medium term, with the gains always being limited in the range of 7.0% (with schooling externalities) to 17.9% (if network effects are accounted for). S tructural Change and Rationalisation As theoretically discussed above, a capital expansion (i.e., employing more people when using the existing technology) can delay a structural transformation. Specifically, structural transformation can be slowed down as weak companies and stagnant sectors can employ immigrant labour at lower wages. This means that the existing economic structure is preserved (Maillat, 1974). There is a further risk that a larger supply of labour will lead to more labour-intensive investments than would have been the case without immigration (Wadensjö, 1973; Elliott, 1991). Examples from small and open economies such as Sweden and Switzerland support this (Lundh & Ohlsson, 1994; Maillat, 1974), and this will slow down the economic growth.4 Newly arrived labour immigrants often get jobs in sectors where there are already many other immigrants. These are often jobs that the natives do not want, such as low-paid, labour-intensive and low-productive jobs (Stark, 1991), which slows down structural transformation in the economy. However, if the labour immigrants are highly skilled, they will instead increase productivity and stimulate structural transformation in the economy (Bodvarsson & van den Berg, 2013). As the focus in economic studies of the economic effects of immigration tends to be placed on labour migrants, little is studied on the economic effects of refugees (Bodvarsson & van den Berg, 2013; Bansak et  al., 2021). Moreover, little is known on how a concentration of 4  Given that a company is profit-maximising, a long-term shortage of one factor product, and thus a long-term price increase for this factor product, leads to this being substituted for another and cheaper factor product. If the factor product is labour, this will be replaced with capital. According to Begg et al. (1987, p. 214), an increase in the price of a factor product leads to substitution effects: ‘The substitution effect leads the firm to produce a given output using a technique which economizes on the factor that has become relatively more expensive. Thus, a rise in the wage rate of labour leads to a substitution effect towards more capital-­ intensive production methods at each output.’ Wonnacott and Wonnacott (1986, p. 723) argue that ‘in a competitive, fully employed economy, the wage rate increases as productivity increases. This conveys a clear message to those producers who can no longer afford the higher wage. The message is: Society can no longer afford to have its scarce labor employed in your activity. There are now too many other, more productive pursuits. This may seem harsh, but it is the sign of economic progress.’

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immigrants in one place impacts the structural changes that occur in that particular place (Lundh & Ohlsson, 1994; see also Mayr, 2012). Human Capital Chiswick and Miller (2009) find overeducation to be more common among recent labour market entrants, while undereducation is more likely to be found among older workers. Among immigrants, greater pre-­ immigration labour market experience is associated with poorer job matches, presumably due to the less-than-perfect international transferability of foreign experience. A longer duration in the US, however, is associated with a lower probability of being overeducated and a greater probability of being undereducated. This is consistent with immigrants being favourably selected for occupational advancement, but this effect becomes realised only after overcoming the disadvantages of the less-than-­ perfect international transferability of their pre-immigration skills. For example, Kangasniemi et al. (2012) found that migration has made a negative contribution to labour productivity growth in Spain and a negative but negligible contribution in the UK. Most of the labour migrants to the US are unskilled, both when it comes to legal labour immigrants and undocumented migrants (Borjas, 1994). The huge labour immigration to Sweden from other Nordic countries during the 1950s and 1960s was dominated by unskilled labour (Fischer & Straubhaar, 1996), as was the labour import from Italy, Yugoslavia, Greece and Turkey (Lundh & Ohlsson, 1999). Three of the four refugee flows studied by Borjas and Monras (2017) were dominated by people with lower human capital than the natives in the area they settled in, and in their study, only the Jewish immigrants to Israel from the Soviet Union had higher human capital than the natives.  axes and Fiscal Transfers T Gochenour and Nowrasteh (2014) demonstrate that an externality of low-income immigration is an increase in the size of the welfare state. Empirical studies on the net transfers between immigrants and natives, and vice versa, indicate that changes have occurred. The transfers from the immigrant population to the native one in Sweden during the 1950s, 1960s and 1970s on 1–2% of the Swedish GDP were replaced by a net transfer from the natives to immigrants on 2% of the GDP in 1994 (Ekberg, 1999). The explanation for this shift is related to the changing nature of immigration from labour migrants to refugees. Later studies

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have identified a continued net transfer from natives to immigrants in Sweden (Gustafsson & Österberg, 2001; Ruist, 2015). If the immigrants to Sweden in 2001 had had the same employment ratio and income structure as the natives, then the GDP would have increased by 1.4% due to the increased tax revenues for the government (Rauhut & Blomberg, 2003). Borjas (2001b) argues that the net transfer from immigrants to the US is marginal, while Simon (1998) argues that the transfer is significant. For Switzerland, immigrants produced a net transfer to the natives until the 1980s (Straubhaar & Weber, 1994). The findings for the UK by Dustmann and Frattini (2014) covering the period of 1995–2011 indicate that immigrants from the European Economic Area (EEA) made a positive fiscal contribution, while non-EEA immigrants made a negative contribution. For immigrants that arrived since 2000, contributions have been positive throughout and particularly so for immigrants from EEA countries. Notable is the strong positive contribution made by immigrants from countries that joined the EU in 2004. Also, in Finland origin matters when it comes to positive or negative transfers in public finances. Immigrants born in the Middle East/North Africa, Southeast Asia and Sub-Saharan Africa received net transfers from the natives in 2011, while immigrants born in South Asia, East Asia, Eastern Europe and Caucasus, Latin America and Western countries produced net transfers to the natives the same year (Salminen, 2019). Wildasin (2008) investigated the trade-off many Western European countries face in the foreseeable future between population ageing and (im)migration, due to fertility rates falling below the replacement rate. He found that skilled and unskilled workers affect the highly redistributive fiscal systems of advanced economies, the first group as net contributors and the second as net beneficiaries. Most immigrants work in low-wage industries, which gives them weak social insurance protection in the event of, for example, unemployment and illness. Furthermore, immigrants risk both a higher level of unemployment and a higher length of unemployment than natives due to their strong connection to low-wage industries which are traditionally unstable (Borjas, 2001a). Katz and Stark (1986, 1987, 1989) as well as Stark and Yitzhaki (1988) discuss what sector the immigrants work in, their income levels and the standard of living in the country in which they live and work. But even if the question of which social insurance policies the immigrants can take advantage of is not explicitly mentioned in the

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above-­mentioned studies, it is still clear that the social insurance cover is limited for labour immigrants compared to natives.5 Immigrants to the US are over-represented among the recipients of welfare and allowances (Borjas, 1999b, 2001b, 2003, 2011a, 2011b, 2018; Blau, 1984). The economic reality in the US for many immigrants clashes hard with their expectations (Borjas, 1994, p. 79): Many immigrants are probably dismayed to find that the roads in Los Angeles and New York are not paved with gold, and that the types of jobs available and the incomes that their skills can command are far below what they expected. As this happens, immigrants are more likely to receive welfare benefits the longer they reside in the United States. (Italics in original)

Economic Consequences at an Individual Level Unemployment and Wages As stated above, immigration increases the size of the labour force. If the number of jobs is constant, more people will compete for fewer jobs. This leads to a shift in the equilibrium situation and a fall in wages (Dustmann et  al., 2013, Fassmann & Münz, 1995; Zimmermann, 1995; OECD, 2022). Incomes will be lower for immigrants, and not only on a shortterm basis (Borjas, 2013). Immigrants will also experience unemployment more often than natives but also find new jobs quicker than natives (Chiswick et  al., 1997). Income changes due to immigration strike primarily against low-income earners (Johnson, 1980). At the same time, the profits for capital owners will increase in the country of immigration (Layard et al., 1992), as will the incomes of the highly educated (Johnson, 1980). According to the above presented Dual Labour Market Theory, immigrants are willing to perform low-status jobs at lower wages than other groups in the labour market. Immigrants do not crowd out domestic labour, according to this theory, but rather take the jobs the domestic 5  According to the Dual Labour Market Theory, work in the lower segment of the labour market means lower wages and that you pay less tax than the average. The labour force in the lower segment will have a greater need for public subsidies, and the working conditions in the lower segment are uncertain and unstable. In the upper segment of the labour market, the opposite is true. As the natives in the labour force are to a greater extent in the upper segment and the immigrants in the lower segment, the migrant labour force’s dependence on allowances, subsidies and transfers will be higher than for the native labour force (Piore, 1979).

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labour does not want, and they accept the jobs at lower wages. As a result, immigrants will be a complementary workforce and will not affect wage formation for the native workforce (Piore, 1979). Language proficiency increases employment chances, reduces unemployment and leads to higher earnings (Dustmann, 1994; Chiswick, 1978). Undocumented immigrants have significantly lower wages than legal immigrants (Borjas, 2017). Studies based on US data indicate that equally skilled immigrants and natives are perfect substitutes (Borjas et al., 2011). The performance in the host country (not just in terms of unemployment and wage level) differs a lot between different ethnic groups (Constant et al., 2009). These differences vary across origins, across destinations and by gender. Immigrant earnings catch up to those of the native born after around 18 years in the destination. Schooling matters more for earnings for women, whereas language skills are relatively more important for men (Adsera & Chiswick, 2007). If refugee migration is disregarded, it is mainly the low-skilled who are prone to migration (Stark & Katz, 1989), and due to their lack of competitiveness in the labour market, they will work fewer hours per year and be unemployed to a greater extent than natives (Stark, 1991). Due to this, immigrants will have lower wages than the natives, which is partly to be explained by their unskilled work, and partly because employers have asymmetric information about their productivity. Especially, wages are kept down until the information about immigrants’ productivity increases (Stark, 1991). Although most studies on the economic consequences of immigration on wages and unemployment mainly focus on labour immigrants, studies have also been made on the effects of refugees on wages and unemployment. Borjas and Monras (2017) analyse the supply shocks of labour on regional labour markets after the Cuban refugee flow to Miami in 1960, the Jewish immigration to Israel from Soviet Union in 1990, the Algerian Independence war in 1962 including both French repatriates and Algerian nationals, and the Yugoslav wars in the 1990s. Their conclusion is that: Despite the obvious differences in the historical, economic, and political forces that motivated the various refugee flows, the use of the same empirical framework to study each of the episodes reveals a common thread in the evidence: Exogenous supply shocks adversely affect the labour market opportunities of competing natives in the destination countries. (Borjas & Monras, 2017, p. 407)

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Refugees have far more difficulties to find a job than other immigrants in Sweden (Åslund et al., 2006; Lundh et al., 2002). In general, network externalities may hamper the matching process between (native) employers and (immigrant) labour, as newcomers do not necessarily have the same networks which can facilitate their job search (Uhlig, 2007). Entrepreneurship and Innovation Self-employment is an important aspect of the employment possibilities for immigrant labour. Immigrants are more likely to be self-employed relative natives with similar skills. Borjas (1986) also finds that the probability of self-employment increases for immigrant labour the longer they reside in the country of destination. Geographic enclaves also offer possibilities for self-employment among persons with a similar national or ethnic background. This is not only valid for the US but also for Europe (Andersson et al., 2021). Being an immigrant in Sweden is not the main driver behind entrepreneurship (Andersson & Larsson, 2014). However, highly skilled immigrants are over-represented in Sweden among immigrant entrepreneurs, usually due to a marginal labour market situation (Sundriyal, 2019). Immigrants can be seen as more likely to start businesses than natives. However, it is not primarily about them being more entrepreneurial, but more about creating better conditions for themselves in the new country (Stark, 1991). There is also a self-selection among labour immigrants: they are, for example, young, willing to take risks, more aggressive in their appearance and may have a top competence in a niche area. The companies they start are characterised by being labour-intensive and not needing very much investment capital to start. When it comes to innovations related to immigration at a regional level in Europe, Ozgen et  al. (2012) find empirical evidence for a cross-­ fertilisation of ideas in urban areas, leading to a contextual environment where more ideas are produced and turned into innovative outputs. But there is no evidence supporting a similar development in rural, peripheral, remote and mountainous areas. Naudé (2016) argues that the expectations about the entrepreneurial and innovative potential of migrants may be disappointed because (1) entrepreneurship promotion is a last-resort policy, (2) entrepreneurs are being overestimated and (3) entrepreneurs are too often allowed to capture policy. Furthermore, small businesses are not creating sufficient jobs, they are not raising labour productivity and immigrant entrepreneurs are

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not productively assimilated, while big businesses are largely a legacy of the past. In his literature review, Kerr (2013) shows that very little is still known about return migration of workers engaged in innovation and entrepreneurship except that it is rapidly growing in importance, but the overall effect that the migration has on the home country remains unclear. Kerr concludes (as have many others) that immigration has been essential for leadership in innovation and entrepreneurship in the US. As a last point, while most studies imply that immigrants are a homogeneous group, little is known about the differences in innovations made by labour immigrants, students or refugees. Housing and Segregation That ethnic groups cluster in certain neighbourhoods does not improve their labour market assimilation in the US. Low-income groups living in low-income areas reduce social and economic mobility (Borjas, 1995b). However, highly skilled persons who belong to disadvantaged groups appear more prone to leave residentially segregated areas relative to low-­ skilled persons. But it is also acknowledged that usually, they have the economic possibilities to move to better neighbourhoods (Borjas, 1998).

2.4  Summary This chapter has provided a general overview of the basic economic theories on immigration. On the one hand, theories focusing on the motivation for immigration have been discussed. Microeconomic considerations such as an expected reliance on higher welfare, the risk of poverty, as well as the costs of immigration have been discussed as being of relevance since the very beginnings of the economic discussions on migration. Macroeconomic perspectives also focus on wage differentials and the demand for low-skilled workers in developed economies that might motivate migration. Moreover, the societal dimension, the relevance of institutions and networks are discussed intensively in theory, as well as the dynamic processes of migration. On the other hand, an overview of the potential effects of immigration on the economy of the receiving country has been presented. While decreases in wages, a potential slowdown in automatisation processes and the public costs of immigration are seen as potential negative effects, an immigration surplus, higher productivity through higher competition, new knowledge and innovation, as well as an increase in  local

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consumption are discussed as being potential positive effects of immigration. But an expected redistribution of income, as well as a potential trigger of internal migration processes, should also be considered. So, what do we know about the economic consequences of immigration, and perhaps just as importantly, what do we not know? As the theoretical considerations and the literature review suggest, the findings do not point in just one direction, but in all directions. It is possible to find empirical evidence that supports the positive economic effects of immigration, as well as the negative. To a large extent, the empirical evidence is based on studies analysing the national level, and not sub-national levels. However, the economic effects of immigration cannot be assumed to be a-spatial; that is, not all types of territories, with different economic structures and different types of immigrants, can be assumed to produce the same results related to immigration. To a large extent, the economic consequences surveyed in this chapter are very sensitive to the assumptions, estimations and even the approximations that are made in the individual studies. We will now turn our attention to the economic consequences of immigration to places beyond the cities—that is to places in peripheral, remote and mountainous regions.

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CHAPTER 3

Migration Beyond the Cities

3.1   Urban-Rural Divide Space, place and location matter when it comes to the evolution and performance of economic activities, as neither economic features are evenly spread across a country nor is the demographic structure and the socio-­ economic profile of the population likely to be identical in all regions and municipalities (Shucksmith et  al., 2009). This heterogeneity applies to regular activities as well as to crisis reaction capacities, and the economic resilience towards external shocks differs significantly between urban areas, on the one hand, and rural, remote or mountainous places, on the other (Giannakis & Bruggeman, 2020). In addition to these measurable differences, ‘the essence of the rural may be more elusive than mere numbers and distance; it may include a feeling or perhaps an idea of what rural is’ (Hjort & Malmberg, 2006, p. 56). Hence, previous literature has often focused on difference, the urban and rural divide, and the dichotomy between rural and urban patterns and characteristics. Nonetheless, recent literature suggests more a blurred line between urban and remote or rural areas (Shucksmith et al., 2009). Accordingly, the traditional definitions used in research are being challenged and may be substituted with a more nuanced picture of the urban-­ rural nexus. In this picture, it is not the formal classification of an area or region that matters, but rather the real living conditions of the people and the prevailing economic conditions. Per example, individuals and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Rauhut et al., The Economics of Immigration Beyond the Cities, https://doi.org/10.1007/978-3-031-30968-7_3

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enterprises in non-urban areas that are close to cities may have a better access to infrastructure or public services than expected due to the classification of the region as being ‘rural’, so biasing research results that refer to very clear-cut distinctions. Despite this partial dissolution of the clear-­ cut distinction between the concepts, for analysing the effects of migration, the varied characteristics of rural, remote and mountainous regions, and urban areas have to be taken into consideration. Generally, it is not primarily the geographic patterns or only the population density that differs between urban and rural areas, but also the labour market conditions, access to infrastructure and public services, and many other indicators. In the European Union, the following picture emerges: While the land coverage of rural areas is about 80% in the EU, these areas are home to only about 30% of the population. Rural and remote areas at the same time display the lowest shares of population in age groups below the age of 50 years in the whole Union (CEC, 2022). Mountain areas also display a relatively low ratio of population to area, as they cover 30% of the land area of the EU but are only home to about 64 million people (13% of the population) (Price, 2016). Moreover, socio-economic differences exist between rural and urban areas in the EU, with the risk of poverty being significantly higher, and access to public infrastructure as well as average education levels being substantially lower in non-urban or mountainous areas. Taking these differences into consideration, it is plausible to assume that the economic consequences of immigration will vary depending on the characteristics of both the regional entity and the socio-economic profile of the immigrants. Thus, the following questions emerge: Firstly, are rural or remote areas attractive for migration, and can migration to rural areas be expected according to economic theory and the characteristics of the spatial units? What differences between urban and rural areas influence the migration decision? Secondly, what effects does migration have specifically in remote or rural areas? Are there joint patterns that can be determined, and how do they manifest?

3.2  Attractiveness of Regions Beyond the Cities for Migrants The Theoretical Perspective Within this chapter we discuss the existing economic theories on the determinants of migration, as outlined in Chap. 2, from a spatial point of view. Drawing on the existing theoretical approaches that aim to explain

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migration, Table 3.1 summarises the effects to be expected according to the respective theory for different spatial areas, strictly speaking for urban areas, on the one hand, and rural and remote areas, on the other hand. Scrutinising the neoclassical migration theory, migration to rural areas would take place only if there is a labour shortage, and the expected income is higher than in the country of origin. This implies that the Table 3.1  Overview of existing theories on the determinants of international migration for different spatial areas Migration theory

Urban Rural Explanation

Neoclassical migration theory

x

(x)

New economics of migration

x

x

Dual/ x segmented labour markets

x

Migration systems or networks theory World system theory

x

Theory of cumulative causation

(x)

(x)

Institutional theory Mobility transition

x

(x)

(x)

x

Source: Own compilation

People would move to regions where expected wages are high > this might be the case in rural areas too—at least compared to the home country; but expected income might be higher in urban regions. People would move to regions where they expect a stable income, due to their responsibilities towards family members in their countries of origin > this could be the case in urban and rural areas compared to the home country. Demand for cheap labour as the relevant factor for international migration in developed countries. Incentive to migrate present in all types of regions within the developed countries. Labour in the upper labour market segment comes from cities and is in demand in cities; labour for the lower labour market segment comes from rural areas and is in demand in the cities. A higher share of migrants in urban areas might attract new migrants, supporting existing networks.

Global perspective; but transportation and communication linkages that ease migration might be primarily situated in urban areas. Migration as a dynamic process; relative deprivation, less jobs in the country of origin, and a demand for labour in host countries as sources; this could be the case in urban and rural areas. Institutions that encourage migration might be more present in cities. Movement from rural to urban areas due to demographic and societal transition.

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probability of individuals/households to migrate to urban regions is higher if the expected income is higher compared to the respective home country. Nonetheless, as rural areas often display structural weaknesses compared to the national average, non-rural regions would likely be preferred by migrants following this approach. The approach of the new economics of migration does not really allow for any statement considering the regional dimension. As wage differentials are not necessary to induce migration, rural and remote areas might be attractive targets if a stable income is to be expected. Nonetheless, one could argue that regions with a lower risk of unemployment might be preferred due to the migrant’s responsibilities towards family members. Moreover, insurance possibilities or a generous social security system might be of even higher relevance than the wage level itself. All these aspects might be easier to overcome in cities. The dual or segmented labour market theory constitutes the demand of developed countries for cheap labour as the relevant factor for international migration. As such, a demand might exist in urban as well as rural areas. The incentive to migrate would be present in all types of regions within developed countries. One could argue that the demand in rural areas might be lower, as the educational level in rural areas is often lower compared to that seen in urban regions. Nonetheless, a lower educational level on average does not mean that low-skilled workers are necessarily more present, and it could also be the result of a lower share of highly educated people. Moreover, the demand for low-skilled workers depends highly on the spatial distribution of industries. The migration systems or network theory seems to provide a more clear-­ cut picture by stating that existing connections between the sending and receiving country lead to migration flows, where the existing networks of migrants in the receiving country are a pull factor for further in-migration. As the share of migrants is typically higher in urban areas, based on this theory, the incentive to migrate would also be much higher in urban areas. The world system theory focuses on the global perspective, and the ties between countries that might motivate migration. Hence, a spatial differentiation on a national level is hard to depict. One argument could be that linkages between the sending countries and towns and cities might be higher compared to rural areas, as transportation and communication centres are primarily situated in urban areas. Nonetheless, these differences between towns or cities, on the one hand, and rural areas, on the other, are not restricted to the production factor of labour, but may also apply to capital as a production factor.

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The theory of cumulative causation understands migration as a dynamic process and provides little interpretation of the differences between urban and rural areas. The relative deprivation of non-migrant families, a lower demand for workers due to investments in the countries of origin and a higher demand for migrants in the host countries are seen as relevant factors which increase migration. Based on institutional theory which highlights the relevance of existing institutions in a regional entity, one could expect more intensive migration to urban areas, as such institutions as smuggler groups or agencies for migration might be more present there. Clear spatial effects of migration are to be expected if mobility transition theory is considered, but not to the favour of remote areas—on the contrary, migration from rural to urban areas is to be expected in both developed and developing countries. Hence, based on the existing theories on the determinants of migration, migration to urban areas seems overall to be more likely than is commonly portrayed. Empirical Evidence on the Regional Preferences of Migrants There is some evidence that metropolitan regions loose population, while remote areas gain population (termed as ‘counter-urbanization’). For example, for the UK ‘there is a relatively strong negative relationship between net internal migration rate and the metropolitan/urban status of places’ (Champion, 2005, p. 9). Nonetheless, in other cases it could be shown ‘that migrants appear more likely to select regions that have large populations (and therefore many amenities), are in close proximity, are relatively isolated and are generally prosperous’ (Pellegrini & Fotheringham, 2002, p. 502). In these cases, ‘destinations with large populations are seen as very attractive’ (Yano et al., 2003). In line with this, when it comes to international migration, there is some evidence that it flows towards more affluent urban areas or general regional entities that already have a high level of prosperity, but less towards economically growing areas (Coombes, 2010). As outlined above, labour market conditions particularly act as a driver for the regional attractiveness appreciated by migrants. The evidence suggests that ‘besides construction, tourism and domestic work, agriculture has played a fundamental role in determining immigration flows’ (Natale et  al., 2019, p.  7) to rural and remote areas. Empirical findings for the European Union indicate that the share of rural employment that is filled by migrants has been increasing over time, with agriculture being the most

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relevant sector of employment. Hence, the share of migrants employed in agriculture in many cases is substantially higher than the share of migrants employed in other sectors. Moreover, compared to the native population, in agriculture, migrants often only have elementary occupations and are temporary workers, and this effect to some extent may be particularly driven by the EU Seasonal Workers Directive (Natale et al., 2019, p. 5). While the availability of fitting jobs is one key driver for migration, beyond the labour market conditions, scholarly research on migration into rural areas suggests different drivers for the migration decision. These factors include the attractiveness of low-cost housing for families or high-end rural estates for wealthy pensioners, as well as other social conditions such as a flight from urban unrest and the attractive ‘rural ideal’ or good environmental conditions. Empirical evidence, for example from Sweden and Austria, indicates that environmental factors such as housing and environmental conditions can be ‘more important for migration than job opportunities’ (Hjort & Malmberg, 2006, p. 56; Aigner-Walder & Putz, 2023). Time-related dynamics play an important role in this context, as mobility and work patterns have changed over the last decades, resulting in behavioural adjustments. One important factor is the more recent increase in individual mobility and public transportation that allows individuals to live in preferred areas, while commuting to areas that display a more diverse and attractive labour market (Eliasson et al., 2003). Moreover, the changing requirements of workplaces matter, and increasing capacities and decreasing the costs of communication technologies, digitalisation and the increased use of home-office solutions in many sectors and jobs increase the flexibility of workers, allowing for long(er) distances between the workplace and the place of living. More recently, in many parts of the EU the ‘access to services was limited for migrants because of pandemic induced lockdowns’, and a ‘deterioration of the socio-economic situation among the host populations’ took place, resulting in additional problems such as decreasing integration efforts or ‘an increase in anti-migration sentiments’ (European Committee of the Regions, 2021: 27). But contrary to this, ‘many migrants have played a critical role in countries’ responses to the pandemic as key workers in essential sectors, such as food processing, delivery, transportation, and haulier services or health care, that were vital for the continuity of economic activity’ (OECD, 2022, p. 9). While large-scale studies are so far missing, it is plausible that rural and remote areas were at least as affected by these developments as urban areas, if not to a stronger extent.

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Thus, regional governments have an original interest in migration issues. The literature suggests that the respective regions themselves can influence their attractiveness for migrants by providing additional regional ‘pull factors’. While this may not be easy due to the budget constraints that apply in many cases, additional external funding or networking with partners from the private or the non-profit sector may support these efforts. As for the EU, there is empirical evidence that not only regions themselves but also EU institutions can purposefully influence migration streams (particularly those of TCN), as public funds and regional policies have a significant effect on the attractiveness for migration in the EU, for example by the creation of ‘job opportunities as well as the supply of high-­quality welfare, which includes education, health, infrastructure, and facilities for welcoming immigrants’ (Cerqua et al., 2022, p. 534). In line with the focus of the EU regional policy to support the least economically developed regions, particularly remote and rural regions may benefit from this finding. To conclude, in scrutinising the existing migration theories, migration to urban areas seems in fact to be more likely; and a secure and higher income compared to the country of origin and existing networks of migrants or institutions that support migrants in entering the local labour market could also increase the attractiveness of rural and remote areas. Thus, depending on the specific given conditions in a regional entity, a wide range of different migrant groups may be attracted by pull factors prevalent in more remote or rural regions.

3.3  Effects of Migration on a Regional Level— Empirical Evidence With a few exceptions, the literature focusing on the spatial effects of international migration is underdeveloped (see, e.g., Tipayalai, 2020).1 The majority of studies focusing on the regional level (theoretical or 1  One of the root causes for the relative scarcity of studies on the regional effects of migration is the lack of comparable data for regional entities, even with the NUTS classification of the European Union, the Territorial Levels 2 and 3 of the OECD and also the LAU classifications for regional entities. The nomenclature of territorial units for statistics (NUTS) is a hierarchical system for dividing up the economic territory of the EU and the UK in three different spatial levels: (1) major socio-economic regions (NUTS1), (2) basic regions for the application of regional policies (NUTS2), and (3) small regions (provinces) for specific diagnoses (NUTS3). To meet the demand for statistics at a local level, Eurostat maintains a system of Local Administrative Units (LAUs) compatible with NUTS. These LAUs are the building blocks of the NUTS, and comprise the municipalities and communes of the European Union.

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empirical) primarily cope with the internal migration that takes place inter-­ regionally, with an increasing amount of comparative literature emerging in the last few decades (e.g., Yano et al., 2003). Hence, it can be assumed that the migration flows studied mainly consist of individuals that are part of the labour force (and their families) and fewer whose labour market participation is restricted due to legal issues or a qualification mismatch. Thus, any conclusions from the literature on internal migration can only partially be generalised towards international migration, as only certain international migrants fulfil the same conditions as internal migrants in the short-term. Nonetheless, given the time-dynamics of migration, international migrants may become internal migrants later on, so that over time the rationales may equalise. Literature suggests that regions can benefit from migration in different ways, not only as a vital source of labour (especially in key sectors with significant shortages) but also in several other dimensions of economic development. The OECD (2022, p. 9f) view that: ‘[…] migration is an important mechanism to boost regional GDP per capita, especially in lower-income regions, thus contributing to within-country economic convergence’. Thus, immigration could be one trigger to revitalise these remote regions economically and to mitigate problems arising from issues such as population ageing (Perlik et  al., 2019; Rye & O’Reilly, 2020; Przytuła & Sułkowski, 2020). Notwithstanding, ‘[…] the link between migration and regional income is ambiguous, as the channels through which migration can affect regional development levels might have positive or negative effects’ (OECD, 2022, p. 99). Looking at the EU27 at a NUTS 2 level, Huber and Tondl (2012) found that migration displays no significant impact on regional unemployment in the EU but has an effect on both GDP per capita and productivity: An increase of one percentage point in immigration in the respective regions increased GDP per capita by about 0.02% and productivity by about 0.03%. In the long-run, the increase raised GDP per capita by 0.44% and productivity by 0.20%. A recent study by the OECD (2022) indicates the contribution of migration to regional economic convergence within and across European countries: On average, a 10% increase in the migrant population share is associated with a 0.15% higher regional income per capita. However, the effects are stronger for lagging regions, especially in lower-income countries within the EU. Overall, for the 25% of poorest regions in a country, the positive effect

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of migration on per capita income is more than twice as high (0.36%). As a result, migration can help poorer regions catch up with the rest of the country, in turn contributing to the income convergence across Europe. (OECD, 2022, p. 99)

Moreover, it is obvious that age and education matter, particularly in regard to the ageing population in many rural and remote areas with a below-average education attainment level. In general, ‘regions with a higher share of migrants tend to be populous places, where both native-­ born and foreign-born workforces have on average a higher level of education’ (OECD, 2022, p.  103). Thus, particularly for remote, rural and mountainous areas, positive effects of migration can be expected. However, these potential positive effects are ambiguous, as underpinned by evidence from other studies (Coppel et al., 2001). These findings should be contrasted against those of Barro and Sala-i-­ Martin (1992), who find limited evidence for the role of migration on regional growth and convergence for Japan and the US, but as they focus on internal migration, this may only to a limited extent be generalisable towards other forms of migration. Also, Kordel and Weidinger (2018, p. xix) conclude that ‘[a]lthough local stakeholders, such as politicians or entrepreneurs, have high expectations in terms of demographic stabilization and the mitigation of labour shortages, there is little evidence of the integration of refugees in rural employment markets’. In most cases, the refugees have to take on self-employment, but with limited success. Thus, the question emerges, which kind of migrants may be particularly beneficial for non-urban regions and how can those groups be attracted?

3.4  Analytical Framework on the Effects of Migration in Non-urban Areas The Case of an Excess Demand for Labour As the domestic labour force is often not interested to work in low-paid, labour-intensive and low-productive jobs with few career possibilities (see e.g., Piore, 1979) or due to demographic developments (such as the ageing of the population), labour needs to be found from abroad to fill the vacancies, which is also commonly described as a labour shortage. A labour shortage occurs when the demand for labour exceeds the labour supply at a specific wage level, and labour shortages can occur in any kind of

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profession, branch or sector. Labour shortages can be present for any qualification level. Thus, it is the regional characteristics that determine in which sectors or industries labour shortages for which qualification level(s) may exist. It is plausible to assume that in areas beyond the cities other sectors may be affected, implying different demands for specific qualifications than in cities. The shortage is said to be relative if the imbalance can be fixed by a change in wages. Otherwise, the shortage is said to be absolute (Eðvarðsson et al., 2007; OECD, 2003). However, absolute labour shortages are very rare as they imply that there are not enough human beings (Rauhut, 2002). If an absolute excess demand for labour (DE) exists in a rural, remote, peripheral or mountainous areas, this will stimulate labour immigration driven by a wage gap between sending and receiving countries (Fig. 3.1). In the context of a labour shortage in the receiving country, the positive effects of immigration in the case of a successful labour market integration are to be expected to outweigh the potential costs of immigration. Nonetheless, an existing excess demand for labour may not be met with an immigrated supply of labour (e.g., due to language problems, missing (recognition of) qualifications, or their legal status and restrictions to work), or the level of immigration may be too low. Consequently, due to labour shortage the production will have to replace labour with capital or move the production elsewhere, with negative economic effects for regions beyond the cities in the latter case.

Fig. 3.1  Labour supply under an inelastic labour demand. (Source: Own compilation)

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From a short-term perspective, the opportunity cost of replacing labour with capital (i.e., investing in new technology) will be too high. If the labour shortage continues or even aggravates over time, the opportunity cost of not replacing labour with capital will also be too high. From a long-term perspective, labour shortage is not about being short of labour but about lacking the capacity to adjust to structural changes in the economy (Begg et al., 1987; Wonnacott & Wonnacott, 1986; Elliott, 1991; Fallon & Verry, 1988). Hence, if an excess demand for labour exists in a rural, remote, peripheral or mountainous area, the bottlenecks in production at the company level will be removed by matching migration. Nonetheless, it may also obstruct the process of structural change in the economy, in the case of less qualified immigration (see Fig. 3.2). At a macro level, a labour immigrant will consume and pay in tax, but the jobs of migrants are still often lower-paid and more insecure. At an individual level, the labour immigrant may experience low-paid, temporal and insecure jobs in the host country, but still be better off than they would be in the country of origin. If the excess demand for labour is not met by the additional supply of labour by immigrants, and a replacement of labour with capital is happening, a tendency towards higher productivity, higher economic growth and higher tax revenues at a macro level can be expected. At a company level, replacing labour with capital will stimulate the structural change in the economy, which will lead to a higher profit share over time. At an individual level, a higher level of human capital is expected, which leads to higher incomes. But in the case of no/not enough matching migration, negative economic effects are to be expected if labour is not replaced with capital (see Fig. 3.2). The Case of an Excess Supply for Labour In line with what Bodvarsson and Van den Berg (2013) note for refugees, it can be argued that any migration by individuals that are neither allowed to work nor have the necessary qualifications is not caused by a demand for labour in the host country (i.e., an excess demand for labour) and not driven by a wage gap between sending and receiving countries, but rather by pull factors related to the respective individual situation. Hence, as soon as these migrants are allowed to work (e.g., with a granted refugee status), there will be an excess labour supply on the labour market in the receiving country, at least in the short-term. In the medium-term, if they

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Fig. 3.2  Migration in the case of an excess demand for labour. (Source: Own elaboration)

are allowed to work and their qualifications become needed (e.g., by a recognition of qualifications and/or education), this may change. This is particularly relevant, as in small labour markets far away from the bigger cities where the economic structure is not that diversified but dependent on a limited set of branches, the labour demand is inelastic. Given that the migration of individuals often displays no matching skills to a rural, remote, peripheral or mountainous area, as long as the labour demand in that area is inelastic, we would end up in a situation SE that is illustrated in Fig.  3.1. In this case, the excess supply of labour has no matching demand. Scrutinising these arguments, the key question is how to deal with an excess supply of labour in areas beyond the cities. Building on the discussion in the existing literature (e.g., Fallon & Verry, 1988; Elliott, 1991), we identify four possible direct labour market effects, depending on the characteristics of the migrants (see Fig. 3.3): . The excess supply of labour will depress wages (see Fig. 3.1). 1 2. The inability of getting a job due to the excess supply of labour will lead to a high relative unemployment for the group causing excess supply, and one can expect labour market exits and inactivity, as below a certain wage level, exit and inactivity are more attractive than working (at least if a social support system exists).

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Fig. 3.3  Migration and excess supply for labour. (Source: Own compilation)

3. If the labour market cannot offer employment to all labour, a higher self-employment can be expected by members in the group causing an excess supply of labour. 4. The excess labour supply with the highest human capital may leave the area for labour markets where their human capital better pays off. We can expect this migration to follow the migration model outlined by Lewis (1954). The first three points result in lower tax revenues, lower consumption and underemployment at a macro level, and accordingly, a net transfer from natives (or more general individuals with jobs) to immigrants (or more general individuals without jobs) will take place. At a company level, the access to low-productive labour will stimulate labour-intensive investments, which obstructs the structural change in the economy. At an individual level, lower incomes, unemployment and a dependence on welfare schemes can be expected. If a local or regional economy does not have sufficient savings to invest in capital or infrastructure, or if its market is too small due to low consumption, then its productivity level will remain low. Limited market expansion, low savings, consumption reduced stock of capital in the economy and low income are all influential factors, and both supply and demand will be too low to trigger any expansion of the local or regional economy. The result is a vicious circle of underdevelopment (Capello, 2016), which is illustrated in Fig. 3.4. Importantly, it has been noted that

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Fig. 3.4  The vicious circle of regional underdevelopment. (Source: Modified after Capello, 2016, p. 104)

many regions beyond the cities struggle with the challenges related to the vicious circle of underdevelopment (Rauhut & Rauhut Kompaniets, 2018). Considering the above-mentioned point, at an individual level, the immigrants will experience an upward socio-economic mobility when they move within the new country, from rural and peripheral areas to bigger cities. However, if they remain in the rural areas, they will experience little upward socio-economic mobility. At a macro level, the out-migration of high-skilled labour to areas where their capital pays off better will result in higher tax revenues, higher consumption and less welfare transfers to immigrants—at least from a national perspective. At a regional level, this will increase the pressure for rationalisation in the regions losing human capital. Furthermore, companies will experience a labour market mismatch where vacancies exist with a simultaneous unemployment, because the labour needed is not complementary with that which is available. Although the regions beyond the cities cannot keep the labour with the highest human capital, they may still find a solution. According to Capello (2016, p. 155), ‘backward regions offer locational advantages due to their relatively lower wages and unit labour costs, and therefor attract capital /…/ which increases the competitiveness of local industry. Traditional labour-intensive manufactures may therefore be advantageous to backward or relatively newly industrialized areas.’ In cities, the situation in the case of an excess supply of labour due to migration will be different. Firstly, the labour market in bigger cities is far

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more diversified, as is the economic structure. Hence, the demand for labour is not inelastic as is the case in rural, remote, peripheral and mountainous areas. More diversified labour markets and economic structures mean that there are more jobs for both high- and low-skilled labour. Most of the entry jobs for immigrants such as unskilled jobs in the manufacturing industry and service sector are found in bigger cities. While remote regions may offer available housing for migrants, the service sector is very small. When discussing immigrant entry jobs in the light of the Central Place Theory (Christaller, 1933; Lösch, 1954), it becomes clear that the available jobs for immigrants in highly diffused places (i.e., in remote and peripheral regions at the lower end of the hierarchy of places) are very few, and the tourism and hospitality sector dominate among these jobs together with agriculture. The high-tech sector is allocated to cities (at the higher end of the hierarchy of places), and so are a majority of the low-qualified jobs in the service sector. Knowledge-intensive sectors will require highly skilled labour from abroad or nationally. While in urban areas low-skilled labour will have the same impact on the labour market and economic structural change as in rural, remote, peripheral and mountainous areas, the high-skilled labour immigrants and refugees will stimulate the structural change in the economy, stimulate higher productivity and hence stimulate economic growth. Theoretically, this is also valid for rural and peripheral areas. However, the bigger the city, the more knowledge-­intensive sectors; while in more rural and peripheral areas, the less knowledge-intensive sectors. This follows the main argument of Lösch (1954). Hence, highly skilled immigrants will be more in demand in cities than in areas beyond the cities, increasing the potential positive effects of new knowledge and innovation in cities, with lower expectations on such positive effects in areas beyond the cities. Other Aspects No matter if an excess supply or demand for labour exists, there will still be demand effects relating to immigration. Immigrants consume as soon as they arrive within a region. Hereby, a certain amount of consumption is independent of income (e.g., food, clothes, housing). The consumption of these goods will increase local consumption and market size in rural regions. Hence, positive economic effects for regions—also beyond the cities—are to be expected. Moreover, immigrants bring knowledge to

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rural regions, which might lead to innovations, new products and connections to new markets, and offer an opportunity for rural areas. Nonetheless, the overall costs of integration for areas beyond the cities still need to be considered.

3.5  Concluding Remarks and Guiding Research Questions Areas beyond the cities (e.g., rural, remote and mountainous areas) display specific characteristics and patterns that distinguish them from more urban areas. These differences manifest not only in social or economic developments but may also hamper international migration. At the same time, migration has the potential to contribute substantially to the economic development of these territories. The stumbling block if this positive impact can be realised is related to the question of whether the international migrants can be absorbed by the regional labour market. Focusing on the effects of migrants in non-urban areas based on existing theoretical and empirical studies, we find that their effect is described as being different compared to the urban space. Based on the theoretical and empirical considerations in Chaps. 2 and 3, our further analysis focuses on the following research questions: • Is there a need for labour in areas beyond the cities? Are there differences to be expected between cities and rural, remote or mountainous areas? How far are migrants already of relevance when it comes to the development of the population in working age? • Do migrants meet the needs of the labour market? How can the educational attainment level of migrants be described especially compared to natives? Are there any differences between cities and areas beyond the cities when viewed from a spatial perspective? • Can we expect a high demand effect from immigrants? Consumption very much depends on income, so how can the income situation of migrants be described, especially in comparison to natives? • Do we see positive effects of immigration on innovation and entrepreneurship? In which areas are migrants self-employed, and how sustainable are their companies?

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• What overall effects of migration on economic growth in regional areas are to be expected? Do we see an immigration surplus? Are there any differences between cities and areas beyond the cities? • And last but not least, who carries the costs of immigration? Are remote and rural areas obliged to carry the costs of integration? Or might the positive effects of immigration succeed in areas beyond the cities, as the costs of immigration are carried by national institutions, leading to a fiscal redistribution between regions? The above-mentioned research questions lead the analysis in Chap. 5, focusing on the effects of immigration in areas beyond the cities with a regional focus on the European Union. Details on methods and data used in the analysis are explained in Chap. 4.

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Perlik, M., Galera, G., Machold, I. & Membretti, A., eds. (2019). Alpine refugees: Immigration at the Core of Europe. Cambridge Scholars Piore, M. J. (1979). Birds of passage. Cambridge University Press. Price, M.  F. (2016). Mountains move up the European Agenda. Mountain Research and Development, 36(3), 376–379. Przytuła, S., & Sułkowski, L., eds. (2020). Integration of migrants into the labour market in Europe: National, organizational and individual perspectives. Emerald. Rauhut, D. (2002). Arbetskraftsbrist och arbetsinvandring—hot eller möjlighet för ekonomisk tillväxt? Institutet för tillväxtpolitiska studier, Rapport A 2002:010. Rauhut, D., & Rauhut Kompaniets, O. (2018). The impact of immigrant entrepreneurship on regional development in Western Sweden. Romanian Journal of Regional Science, 12(1), 18–42. Rye, F.  J., & O’Reilly, K. (Eds.). (2020). International labour migration to Europe’s rural regions. Routledge. Shucksmith, M., Cameron, S., Merridew, T., & Pichler, F. (2009). Urban-rural differences in quality of life across the European Union. Regional Studies, 43(10), 1275–1289. https://doi.org/10.1080/00343400802378750 Tipayalai, K. (2020). Impact of international labor migration on regional economic growth in Thailand. Journal of Economic Structures, 9(15). https://doi. org/10.1186/s40008-­020-­00192-­7 Wonnacott, P., & Wonnacott, R. (1986). Economics. McGraw-Hill. Yano, K., Nakaya, T., Fotheringham, A. S. T., Openshaw, S., & Ishikawa Yoshitaka, I. (2003). A comparison of migration behaviour in Japan and Britain using spatial interaction models. International Journal of Population Geography, 9, 419–431.

CHAPTER 4

Data and Methods

4.1   Research Framework This volume discusses the economic consequences of immigration in areas beyond the cities. Geographically, the European Union is the research area of interest. As EU citizens have the same civic rights as natives in other EU countries (with the exception to vote for the national parliament), according to the Maastricht Treaty (European Union, 1992), EU citizens are not considered as migrants in our study. EU citizens can enter the labour market directly and they are not subject for any integration policies within the EU countries (Mügge & van der Haar, 2016). Hence, we only focus on non-EU citizens when talking about immigrants. In this context, the term third country nationals (TCN) is used. According to the European Commission (n.d.), TCN reside legally in the EU and are the target of EU integration policies. A 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)’ (European Commission, n.d.). There are two special cases to be considered: Great Britain is no longer part of the European Union, but migrants from Great Britain have been EU citizens until January 2020. As we use primarily data up to the year 2019, British migrants were still counted as EU citizens in the data set. Thus, non-EU28 citizens are our focus group, but Great Britain as a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Rauhut et al., The Economics of Immigration Beyond the Cities, https://doi.org/10.1007/978-3-031-30968-7_4

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country is not covered by our analysis. Secondly, Iceland, Liechtenstein and Norway are part of the European Economic Area (EEA), which unites them as countries of the European Free Trade Association (EFTA) with the Member States of the EU in an internal market with the free movement of goods, people, services and capital.1 Hence, citizens of these three countries should be counted as EU citizens according to our definition. Unfortunately, there is no data available for citizens of the EEA, meaning that there is some inconsistency in our analysis regarding this point. But as the relevance of immigration from the three countries to the EU is rather negligeable compared to all the other world regions, the associated bias is expected to be insignificant. The analysis follows a two-step approach: On the one hand, we scrutinise data for the European Union as a whole to show and compare national differences (e.g., on population projections), as well as to analyse regional differences within the EU countries (e.g., on the development of populations in working age). Secondly, we zoom into a set of EU countries (Austria, Bulgaria, Finland, Germany, Italy, Spain and Sweden) and discuss a number of case studies in rural, peripheral, remote and mountainous regions.2 The selection of these countries and case studies is based on that used in the Horizon 2020 (H2020)-funded MATILDE project.3

 Switzerland is also one of the EFTA countries but does not take part in the EEA (European Parliament, 2022). 2  The following NUTS 3 regions are studied AT211 Klagenfurt-Villach, AT341 Bludenz-­ Bregenzer Wald, BG422 Haskovo, DE215 Berchtesgadener Land, DE21D Garmisch-­ Partenkirchen, ES241 Huesca, FI195 Pohjanmaa (Österbotten), FI1D3 Pohjois-Karjala, ITC11 Torino, ITH20 Trento, SE312 Dalarnas län. In some cases, data is only available at NUTS 2 level, and therefore we have used the data for the NUTS 2 regions of the studied NUTS 3 regions belonging to: AT21 Kärnten, AT34 Vorarlberg, BG42 Yuzhentsentralen, DE21 Oberbayern, ES24 Aragón, FI19 Länsi-Suomi, FI1D Pohjois-ja Itä-Suomi, ITC1 Piemonte, ITH10 Provincia Autonoma di Bolzano/South Tyrol and SE31 Norra Mellansverige. 3  Details considering the MATILDE project as well as the selected countries and case studies can be found in Kordel and Membretti (2020). The results presented here only focus on those countries belonging to the European Union. Hence, Great Britain, Norway and Turkey have been excluded from the analysis. Apart from legal differences within the mentioned countries, problems relating to data availability and comparability were also relevant for this decision. For details considering the project as well as an overview of all deliverables, see https://matilde-migration.eu. 1

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The time period covered ranges from 2009 (partly also from 2001) to 2019. Some analyses are based on the whole period, using different years for comparative approaches, while some only focus on 2019  in a crosscountry comparison. For projections, the time period up to 2050 is considered.

4.2   Data The empirical material scrutinised in this volume comprises both qualitative and quantitative information and data. The data was derived from publicly available sources such as Eurostat and was also generated in the context of the already mentioned Horizon 2020 project MATILDE. The qualitative data that is used in the analysis mainly originates from the different deliverables from the MATILDE project and has previously been published in the respective project deliverables. This data consists of results of interviews and focus groups undertaken with different stakeholders from the public sector, private sector, citizens or non-profit organisations. In addition, TCN were interviewed and participated in focus groups. The interviewers followed a semi-structured interview guideline, and the focus groups discussed pre-decided themes. The interview guidelines and the focus group themes varied depending on whether the interviewed persons or focus groups were discussing social or economic impacts of TCN immigration to areas beyond the cities (e.g., Kordel et  al., 2020). Standard ethical principles were applied.4 The surveyed economic dimensions contained four dimensions: (1) economic growth, considering TCN’s contributions to the GDP and fiscal systems at different territorial scales; (2) impact on national and regional labour markets, considering participation rates (long-term), unemployment rates, part-time jobs, seasonal work and interactions with nested markets; (3) productivity and innovation inside organisations and companies, as well as social innovation practices; (4) development of entrepreneurship and social entrepreneurship considering the share of enterprises funded by TCN and the survival rates of these firms.5 This part of the data 4  The interviews and focus group activities complied with the ethical principles outlined in the European Code of Conduct for Research Integrity (Allea, 2017), with particular reference to the Guidance note on Research on refugees, asylum-seekers and migrants (CEC, 2020). In some EU Member States, national ethical principles were complied with as well. 5  The surveyed social dimensions contained Social polarisation, Social cohesion, Active participation and citizenship rights, and Access to and quality of services.

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also comprises the qualitative assessments of statistical data (Aigner-­ Walder, Luger, et al., 2021a; Aigner-Walder, Lobnig, et al., 2021b; Laine & Rauhut, 2021). The quantitative data is based on different sources. Firstly, data from the MATILDE project was re-used, particularly a data set on regional indicators that was constructed for the quantitative analysis (Aigner-­ Walder, Lobnig, et al., 2021b.), as well as information drawn from statistical briefings that analyse the economic impact of migration in the countries that were covered by the MATILDE project (Aigner-Walder, Luger, et al., 2021a; Laine & Rauhut, 2021). Additionally, a new data set was compiled based on data obtained from Eurostat.

4.3   Indicators This subchapter describes the indicators used for the constructed database. The data was used in different ways. The indicators were used directly for analyses, and based on the available data, dummy variables were created for the statistical analysis, reducing the spatial or content-related dimensions as will be covered in more depth below (see Table 4.1). The Spatial Dimension. Focusing on areas beyond the cities, the spatial classification of regions deserves specific attention. As discussed in Chap. 3, a simple rural-urban divide does not capture the full picture of regional diversity which is the focus of this book, but the spatial classification allows for a more detailed differentiation. In other words, it is less a formal classification of a region as an urban or rural environment that matters, but more the real levels of accessibility of public services and infrastructure for citizens and the related economic structure that makes the difference for the everyday lives of people, as well as for the potential economic performance. Thus, different definitions and therewith types of non-urban areas were considered to capture as many details as possible and used to test the robustness of the findings. Finally, we focus on the difference between metropolitan and non-metropolitan areas to empirically underpin our main argument. In this volume, we use the Eurostat (2023) typology of metropolitan and non-metropolitan regions to categorise regions with big cities and regions beyond the cities. Metropolitan regions are approximations of functional urban areas of 250,000 or more inhabitants including their commuting zones. This kind of metropolitan region is found around a country’s capital and second-tier cities. Those regions which do not

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Table 4.1  Overview of variables Abbreviation

Full indicator

emp_total

Employment rate total age 15–64

emp_EU28

emp_NEU28

emp_RC

unemp_total

unemp_EU28

unemp_NEU28

unemp_RC

edu_tert_total

edu_tert_EU28

edu_tert_NEU28

Data source

Employment rates by sex, age, educational attainment level, country of birth and NUTS 2 regions (LFST_R_LFE2EMPRC) Employment rate EU28 age Employment rates by sex, age, 15–64 educational attainment level, country of birth and NUTS 2 regions (LFST_R_LFE2EMPRC) Employment rate non-EU28 Employment rates by sex, age, nor reporting country age educational attainment level, 15–64 country of birth and NUTS 2 regions (LFST_R_LFE2EMPRC) Employment rate reporting Employment rates by sex, age, country age 15–64 educational attainment level, country of birth and NUTS 2 regions (LFST_R_LFE2EMPRC) Unemployment rate total age Unemployment rates by sex, age, 15–64 country of birth and NUTS 2 regions (LFST_R_LFUR2GAC) Unemployment rate EU28 age Unemployment rates by sex, age, 15–64 country of birth and NUTS 2 regions (LFST_R_LFUR2GAC) Unemployment rate Unemployment rates by sex, age, non-EU28 nor reporting country of birth and NUTS 2 country age 15–64 regions (LFST_R_LFUR2GAC) Unemployment rate reporting Unemployment rates by sex, age, country age 15–64 country of birth and NUTS 2 regions (LFST_R_LFUR2GAC) Educational attainment Population by educational (tertiary education) total age attainment level, sex, age, country 15–64 of birth and NUTS 2 regions (%) (EDAT_LFS_9917) Educational attainment Population by educational (tertiary education) EU28 age attainment level, sex, age, country 15–64 of birth and NUTS 2 regions (%) (EDAT_LFS_9917) Educational attainment Population by educational (tertiary education) non-EU28 attainment level, sex, age, country nor reporting country age of birth and NUTS 2 regions (%) 15–64 (EDAT_LFS_9917) (continued)

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Table 4.1  (continued) Abbreviation

Full indicator

Data source

edu_tert_RC

Educational attainment (tertiary education) reporting country age 15–64

gdp_pps_NUTS2

Regional gross domestic product (PPS per inhabitant) by NUTS 2 regions EU27 Population total 15 years or over

Population by educational attainment level, sex, age, country of birth and NUTS 2 regions (%) (EDAT_LFS_9917) Regional gross domestic product (PPS per inhabitant) by NUTS 2 regions (TGS00005) Population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Calculated using: population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Calculated using: population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Population on 1 January by age, sex and NUTS 2 region (DEMO_R_D2JAN) Population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN)

pop_total_1000

pop_ NEU28_1000 pop_for_1000

Population non-EU28 nor reporting country 15 years or over Population foreign country 15 years or over

pop_NEU28_pct

Population non-EU28 nor reporting country 15 years or over

pop_for_pct

Population foreign country 15 years or over

pop_total_ ag_1000

Population total all ages

pop_15-­64_1000_ Population aged 15–64 total total pop_15-­64_1000_ Population aged 15–64 EU28 EU28 pop_15-­64_1000_ Population aged 15–64 NEU28 non-EU28 nor reporting country pop_15-­64_1000_ Population aged 15–64 RC reporting country

(continued)

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Table 4.1  (continued) Abbreviation

Full indicator

Data source

pop_15-­64_pct_ total

Population aged 15–64 total

pop_15-­64_pct_ EU28

Population aged 15–64 EU28

pop_15-­64_pct_ NEU28

Population aged 15–64 non-EU28 nor reporting country

pop_15-­64_pct_ RC

Population aged 15–64 reporting country

Calculated using: population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Calculated using: population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Calculated using: population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Calculated using: population by sex, age, citizenship, labour status and NUTS 2 regions (LFST_R_LFSD2PWN) Population by educational attainment level, sex, age, country of birth and NUTS 2 regions (%) (EDAT_LFS_9917) Population by educational attainment level, sex, age, country of birth and NUTS 2 regions (%) (EDAT_LFS_9917) Population by educational attainment level, sex, age, country of birth and NUTS 2 regions (%) (EDAT_LFS_9917)

edu_prim_total

Educational attainment (less than primary, primary and lower secondary education [levels 0–2]) total 15–64 edu_prim_EU28 Educational attainment (less than primary, primary and lower secondary education [levels 0–2]) EU28 15–64 edu_prim_NEU28 Educational attainment (less than primary, primary and lower secondary education [levels 0–2]) non-EU28 nor reporting country 15–64 edu_prim_RC Educational attainment (less than primary, primary and lower secondary education [levels 0–2]) reporting country 15–64 edu_sec_total Educational attainment (Upper secondary and post-secondary non-tertiary education [levels 3 and 4]) total country 15–64

Population by educational attainment level, sex, age, country of birth and NUTS 2 regions (%) (EDAT_LFS_9917) Population by educational attainment level, sex, age, country of birth and NUTS 2 regions (%) (EDAT_LFS_9917) (continued)

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Table 4.1  (continued) Abbreviation

Full indicator

Data source

edu_sec_EU28

Educational attainment (upper secondary and post-secondary non-tertiary education [levels 3 and 4]) EU28 country 15–64 Educational attainment (upper secondary and post-secondary non-tertiary education [levels 3 and 4]) non-EU28 nor reporting country 15–64 Educational attainment (upper secondary and post-secondary non-tertiary education [levels 3 and 4]) reporting country 15–64 Real growth rate of regional gross value added (GVA) percentage change on previous year

Population by educational attainment level, sex, age, country of birth and NUTS 2 regions (%) (EDAT_LFS_9917)

edu_sec_NEU28

edu_sec_RC

gva_pct_change

prim_income_pps

Primary income of private households—balance of primary incomes/national income, net; purchasing power standard (PPS)/inhabitant bussiness_dem Business demography—birth rate: number of enterprise births in the reference period (t) divided by the number of enterprises active in t— percentage: industry, construction and services except insurance activities of holding companies hrst_pct_total_pop Human Resources in Science and Technology (HRST) by category and NUTS 2 regions; percentage of total population hrst_pct_active_ HRST by category and NUTS pop 2 regions; percentage of population in the labour force

Population by educational attainment level, sex, age, country of birth and NUTS 2 regions (%) (EDAT_LFS_9917) Population by educational attainment level, sex, age, country of birth and NUTS 2 regions (%) (EDAT_LFS_9917) Real growth rate of regional gross value added (GVA) at basic prices by NUTS 2 regions—percentage change on previous year (NAMA_10R_2GVAGR) Primary income of private households by NUTS 2 regions (TGS00036)

Business demography and high growth enterprise by NACE Rev. 2 and NUTS 3 regions (BD_HGNACE2_R3)

HRST by category and NUTS 2 regions (HRST_ST_RCAT)

HRST by category and NUTS 2 regions (HRST_ST_RCAT) (continued)

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Table 4.1  (continued) Abbreviation

Full indicator

Data source

internet_access

Percentage of households with access to the internet at home

gerd_euro_ inhabitant

Gross Domestic Expenditure of R&D (GERD) all sectors of performance and NUTS 2 regions; Euro per inhabitant GERD all sectors of performance and NUTS 2 regions; percentage of gross domestic product (GDP)

Households with access to the internet at home (ISOC_R_IACC_H) GERD by sector of performance and NUTS 2 regions (RD_E_GERDREG) GERD by sector of performance and NUTS 2 regions (RD_E_GERDREG)

gerd_pct_gdp

Source: Own compilation of data from Eurostat

belong to a metropolitan region are simply considered as non-­metropolitan regions. Mountainous regions are considered as being beyond the cities, as are sparsely populated regions. While these two groups are used separately for some analyses, they were combined to create a dummy that indicates a non-metropolitan area. In the analyses, this dummy is attributed the value of (1) once a region is metropolitan and (0) for all non-­ metropolitan regions. The typology by Dijkstra and Poelmann (2008) could be seen as an alternative typology for this study and is based on settlement patterns. A predominantly rural or intermediate region is assumed to be remote if less than half of its residents can drive to the centre of a city of at least 50,000 inhabitants within 45 minutes. If more than half of the region’s population can reach a city of at least 50,000, it is considered as close to a city. In contrast to more urban areas, intermediate remote regions and predominantly rural remote regions could be considered to be beyond the cities. However, we find it difficult to consider cities with a population size of 50,000–100,000 as bigger cities. Few of these cities can be considered as second-tier cities, and some would qualify for smaller-tier cities, but not as growth poles. Hence, we find the Eurostat typology on metropolitan and non-metropolitan regions better suited for this study. Population. In addition to the indicators for the spatial dimension, indicators for the population demographics were used, with a differentiation between nationals (reporting country), migrants from the European

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Union (EU28: see description above) who hold the same rights as citizens of the respective country when it comes to matters such as labour market access and migrants from Third Countries (TCN) which are subject to specific regulation in most countries. To indicate the percentage of TCN migrants, the percentage of the population that is neither from the EU28 nor the reporting country was taken. In addition, a second indicator was created by summing up all types of migrants and which comprises the share of the population that stems from a foreign country and does not have the citizenship of the reporting country. The Labour Market. Indicators capturing the labour market are included and in detail the share of employment and unemployment for different groups (natives/reporting country, EU28 citizens, TCN, as well as the total). Education. To capture additional population characteristics, indicators on education are examined, and in detail the educational attainment for primary, secondary and tertiary education as a percentage for each group (natives/reporting country, EU citizens, TCN as well as the total). Economic Performance. To capture the economic performance of a region, the regional gross domestic product (GDP) in purchasing power standard (PPS) per inhabitant was included, as well as the real growth rate of regional gross value added (GVA) (at basic prices; percentage change on previous year). Additionally, the primary income of private households was included. As detailed data about economic activity is weak on a regional level, business demography was included as a proxy, and this indicator comprises the birth rate of enterprises, meaning the number of enterprise births in the reference period (t) divided by the number of enterprises active in period t in percentage. The sectors covered are industry, construction and all services except the insurance activities of holding companies. Additionally, households with access to the internet at home were covered; this indicator was taken as a proxy to infrastructure. Moreover, total intramural expenditure on R&D performed annually were included; the measures cover the Euro per inhabitant as well as the percentage in gross domestic product (GDP). Moreover, persons with tertiary education (ISCED)6 and/ or employed in science and technology were covered, both as a percentage of total population and the percentage of population in the labour force. The following table gives an overview of the indicators used, their abbreviations as well as the data source. 6  The International Standard Classification of Education (ISCED) is the international classification for organising education programmes and related qualifications by levels and fields.

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4.4  Methods Data in descriptive statistics that cover EU, national and regional levels was used to reveal significant differences between various spatial types. Additionally, tests for group differences were also conducted using a t-test (or Student’s t-test), which is an inferential statistic to determine if there is a significant difference between the arithmetic means of two different groups. To discover potential relationships between different indicators (in particular between the share of migrants and regional economic patterns), correlation analysis was applied, complemented by graphical plots to reveal links between the different variables. All the tests and analyses were conducted using the software SPSS 28.

4.5  Conclusion The relative scarcity of studies on the effects of migration is inter alia based on the limited accessible data on regional patterns. Drawing from different sources from Eurostat as well as from research projects, we constructed a data base and specific indicators that provide the basis for the subsequent empirical analyses. A particular focus is on the spatial dimension in its different elaborations, where we distinguish between regions beyond the cities and those close to or which are a city.

References Aigner-Walder, B., Luger, A., Schomaker, R.M., et al. (2021a). Economic impact of migration—Statistical briefings. Deliverable 4.3. Retrieved August 31, 2022, from https://doi.org/10.5281/zenodo.4817376 Aigner-Walder, B., Lobnig, C., Luger, A., & Schomaker, R.M. (2021b). A comparative analysis of the migration phenomenon: A cross-country quantitative analysis of the 10 country reports on migrants’ economic impact in the MATILDE regions. Deliverable 4.4. Retrieved August 31, 2022, from https://doi. org/10.5281/zenodo.5017818 Allea. (2017). European Code of Conduct for research integrity. Retrieved October 31, 2022, from https://allea.org/code-­of-­conduct/ CEC. (2020). Guidance note on research on refugees, asylum seekers and migrants. Retrieved October 31, 2022, from https://ec.europa.eu/research/participants/data/ref/h2020/other/hi/guide_research-­refugees-­migrants_en.pdf

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Dijkstra, L., & Poelmann, H. (2008). Remote rural regions. How proximity to a city influences the performance of rural regions. Regional Focus No. 01/2008. Retrieved September 26, 2022, from https://ec.europa.eu/regional_policy/ sources/docgener/focus/2008_01_rural.pdf European Commission. (n.d.). Third-country national. Retrieved January 2, 2023, from https://home-­affairs.ec.europa.eu/pages/glossary/third-­country-­ national_en European Parliament. (2022). The European Economic Area (EEA), Switzerland and the North. Fact Sheets on the European Union—2022. Retrieved January 2, 2023, from https://www.europarl.europa.eu/ftu/pdf/en/FTU_5.5.3.pdf European Union. (1992). The Treaty of the European Union / Treaty of Maastricht. Signed in Maastricht, the Netherlands, 7 February 1992. Official Journal of the European Communities (92/C 191 /01 ). Eurostat (2023). Territorial typologies for European cities and metropolitan regions. https://ec.europa.eu/eurostat/statistics-explained/index.php?title= Territorial_typologies_for_European_cities_and_metropolitan_regions#A_ typology_of_metro.28politan.29_regions [Accessed on 3.1.2023]. Kordel, S., & Membretti, A., Eds. (2020). Classification of Matilde regions. Spatial specificities and third country nationals distribution. Matilde Deliverable 2.1. Retrieved January 5, 2023, from https://matilde-­migration.eu/wp-­content/ uploads/2020/08/MATILDE_D21_Classification_on_spatial_specificities_ and_TCNs_distribution_040820.pdf Kordel, S., Membretti, A., Aigner-Walder, B., Rauhut, D., Schomaker, R., et  al. (2020). Report on data collection framework—MATILDE matrix. Deliverable 2.6. Retrieved August 31, 2022, from https://doi.org/10.5281/zenodo.4009184 Laine, J., & Rauhut, D., Eds. (2021). Ten statistical briefings on immigration’s social impacts. MATILDE deliverable 3.2. Retrieved August 31, 2022, from https://doi.org/10.5281/zenodo.4726634. Mügge, L., & van der Haar, M. (2016). Who is an immigrant and who requires integration? Categorizing in European policies. In B. Garcés-Mascareñas & R. Penninx (Eds.), Integration processes and policies in Europe (pp. 77–90). Springer.

CHAPTER 5

Immigration Beyond the Cities: An Analysis

5.1   Labour Market Needs in Rural Areas—An Ageing Europe The European Union, as well as the majority of countries worldwide, faces demographic change in the sense of an ageing population. The ageing of the population is a consequence of decreasing birth rates and an increasing life expectancy. Besides Japan, the ageing of the population is most advanced within Europe. With more than 36%, Monaco faces the highest proportion of people aged 65 years of age and more, followed by Japan (29.8%), Italy (23.7%) and Finland (22.9%). In total, 18 of the 20 ‘oldest’ countries in the world (measured by the proportion of people aged 65 years and more) are European countries (World Bank, 2022). The higher share of older people and the lower share of young people lead sooner or later to a negative natural population development, meaning that more people die than are born within a year. Subsequently, population growth is, if even possible, depending on migration. For the European Union as a whole, the population is already projected to decrease by 1.4% till 2050. But there are huge differences existing between the Member States: While we see projected decreases in population of more than 20% or close by in Latvia (−26.9%), Lithuania (−23.5%), Romania (−19.6%) and Bulgaria (−18.6%), the population in Malta (+31.8%), Ireland (+25.1%) and Luxembourg (+22.8%) is anticipated to rise rapidly within the coming 30 years (see Table 5.1). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Rauhut et al., The Economics of Immigration Beyond the Cities, https://doi.org/10.1007/978-3-031-30968-7_5

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Table 5.1  Population projection, EU countries, 2020–2050 Population (1000s)

EU (27) Belgium Bulgaria Czechia Denmark Germany Estonia Ireland Greece Spain France Croatia Italy Cyprus Latvia Lithuania Luxembourg Hungary Malta The Netherlands Austria Poland Portugal Romania Slovenia Slovakia Finland Sweden

2020

2050

447,671 11,507 6950 10,694 5812 83,135 1330 4967 10,697 47,321 67,197 4056 60,287 887 1907 2794 626 9772 507 17,405 8904 37,941 10,291 19,281 2095 5458 5527 10,323

441,221 11,927 5655 10,530 6098 82,670 1256 6213 9503 49,349 70,011 3393 58,125 1046 1395 2138 769 9270 668 18,142 9346 34,102 9375 15,503 2044 5147 5291 12,254

Population development (in %) 2020–2050 −1.4 3.6 −18.6 −1.5 4.9 −0.6 −5.5 25.1 −11.2 4.3 4.2 −16.4 −3.6 17.9 −26.9 −23.5 22.8 −5.1 31.8 4.2 5.0 −10.1 −8.9 −19.6 −2.5 −5.7 −4.3 18.7

Source: Eurostat (2021a). Own compilation

As Table 5.2 shows, the majority of the EU countries depend on migration as single source for population growth, as they face a natural population decline, with more deaths than births per year. In 2020, this was also the case for the EU27 in total. Croatia, Italy, Latvia and Romania not only face a natural population decline but also a migration deficit, meaning that more people are emigrating from, exemplarily, Croatia than migrating to the country. A migration surplus, as well as a natural population growth, is registered for Denmark, Ireland, France, Cyprus, Luxembourg, Malta,

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Table 5.2  Source for population development, EU countries, 2020

Migration surplus

Natural population growth

Natural population decline

Denmark, Ireland, France, Cyprus, Luxemburg, Malta, the Netherlands, Sweden

EU27, Belgium, Bulgaria, Czechia, Germany, Estonia, Greece, Spain, Lithuania, Hungary, Austria, Poland, Portugal, Slovenia, Slovakia, Finland Croatia, Italy, Latvia, Romania

Migration deficit Source: Eurostat (2021a). Own compilation

the Netherlands and Sweden. Within these countries, demographic change is less advanced. Scrutinising the regional dimension and looking at our selected countries, there are huge differences within the single countries considering population development. In Austria, Finland, Germany and Spain, we find the highest population growth in the metropolitan areas of the capital and other big cities (e.g., Vienna and Graz in Austria, Helsinki in Finland, Berlin and Munich in Germany, Madrid and Barcelona in Spain). Contrarily, a population decrease is visible in many, rather rural areas (e.g., southern regions in Austria, eastern and northern regions in Finland, eastern regions in Germany, western regions in Spain). For Italy, a north-­ south divide is visible, with population growth in the north and a population downturn in the south. In Bulgaria, only the cities of Sofia and Varna are still growing, while all other Bulgarian regions face a population decline. Contrarily, we see a population increase in almost all regions in Sweden, with the exception of the most northern one (Norrbotten), and again highest increases in the cities of Uppsala and Stockholm (see Aigner-­ Walder, Luger, & Schomaker, 2021 for details). A migration surplus can (but does not necessarily have to) compensate for a negative natural population development. However, migration can rarely stop the ageing of the population, as migrants get older too and adjust their fertility to the host country’s patterns. Nevertheless, migration can slow down the demographic ageing process and especially be a relevant source for labour supply. Hereby, the European or national situation is to be evaluated a little bit differently compared to the regional situation: The immigration needed to counteract demographic ageing in Europe, as well as many countries, would be so numerous that it would simply not be realistic (Gaspar et  al., 2005). Recent research has

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concluded that while immigration from outside Europe will be needed to mitigate some of the bottlenecks in the labour market, immigration cannot solve the problem of demographic ageing (OECD, 2022). At a regional level, the situation might be dissimilar, as internal migration processes are also of relevance. International migration—together with internal migration—might regionally be able to mitigate the bottlenecks in the labour market and postpone demographic change. But there would be winning and losing regions, with the latter facing even bigger problems and presumably mainly situated in rural areas. Within most of the countries we see a clear rural-urban migration between 2002 and 2020 (Aigner-­ Walder, Luger, & Schomaker, 2021). According to the delineated population developments, migration is especially needed beyond the cities to guarantee the labour supply. As we can see below, TCN (third country nationals) already make up a relevant proportion within European regions, and this is significantly higher in metropolitan regions (see Fig. 5.1). Taking a more detailed look at the population aged between 20 and 64 years, the situation within the European countries is not that positive. Even in countries with a natural population growth such as Sweden, working-­age population would already have decreased in most of the regions without immigration. Interestingly, the migration of third country

Fig. 5.1  Share of TCN in and beyond the cities. (Source: Own compilation)

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nationals was even positive in the most northern regions in Sweden of Norrbotten and Västerbotten, where we see a high decrease in natives and no migration of EU citizens. In Italy, without migration all the NUTS 2 regions except South Tyrol would have lost in working-age population from 2007 to 2019. The same is the case for Finland, with the exception of Helsinki. In Bulgaria and Germany, without immigration, all regions would have experienced a downturn of population aged between 20 and 64 years (see Aigner-Walder, Luger, & Schomaker, 2021 for details). The differences in the share of population in working age, 15–64 years, differ less than expected between metropolitan and non-metropolitan regions (Fig. 5.2). The ageing of the population can be seen as a challenge for economic growth, due to the ageing and decrease in population in working age, with potential negative effects on individual and aggregate productivity. Diverse studies show a significant positive effect of an increase in the share of population in working age on economic growth, as seen during the last decades and also known as first demographic dividend (Bloom & Canning, 2005; Kelley & Schmidt, 2005; Lee, 2016; Lindh & Malmberg, 1999; Sheiner, 2014). Correspondingly, negative trends in economic growth are projected due to the decrease in working population (Maestas et al., 2016; Martins et  al., 2005; OECD, 2011; Prskawetz & Lindh, 2007), as expected and already faced in European countries and regions nowadays, 80

pop_15-64_pct_total

70 60 50 40 30 20 10 0

Metropolitan Region

Non-Metropolitan Region

Fig. 5.2  Share of population 15–64  years old in metropolitan and non-­ metropolitan regions (2019). (Source: Own compilation)

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and also underpinned by our data for the link between GDP and working-­ age population. Nonetheless, technological advancements, higher education, etc., are likely to have the potential to compensate for the ageing and shrinking population in working age (Easterlin, 1996; Gaspar et al., 2005; Rostow, 1998; Rauhut, 2002), see also the close link between tertiary education and GDP per capita shown in our data in Fig. 5.3. The studied case study regions (regions beyond the cities) have several common characteristics. All regions report a labour shortage, and the demand for labour is especially troublesome when it comes to unqualified labour. Moreover, much of the labour shortage is related to seasonal work with temporal contracts, and the vacancies are found in low-productive, labour-intensive, low-salary jobs in agriculture and fishing, manual manufacturing jobs, and the service sector. Consequently, immigrants are needed to fill the vacancies (Gilli & Membretti, 2021b; Gruber, Machold, et  al., 2021; Hansson et  al., 2021; Kordel, Weidinger, & Güller-Frey, 2021; Krasteva et al., 2021; Lardies-Bosque & del Olmo-Vicén, 2021a; Rauhut et al., 2021).

R2 Linear = 0,326 80000,00

gdp_pps_NUTS2

70000,00 60000,00 50000,00 40000,00 30000,00 20000,00 10000,00

,00

10,00

20,00

30,00

40,00

50,00

edu_tert_total

Fig. 5.3  GDP per capita at NUTS 2 regions by share in tertiary education for all regions. (Source: Own elaboration)

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While the population projections suggest that the working-age population will constitute a smaller share of the total population, we must be aware of the fact that they do not say anything about the economic structure and the demand for labour. This perceived labour demand may be illusionary and delusive; and to base the future labour demand on a simple headcount exercise, without including technological, organisational or institutional changes, is, at least to us, questionable. Notwithstanding, the stakeholders interviewed in the case study regions pointed at a labour shortage, commonly defining it as when the employers did not get the labour they needed (Gilli & Membretti, 2021b; Gruber, Machold, et al., 2021; Hansson et  al., 2021; Kordel, Weidinger, & Güller-Frey, 2021; Krasteva et al., 2021; Lardies-Bosque & del Olmo-Vicén, 2021a; Rauhut & Enlund, 2022). Again, this voices a perceived labour shortage. However, in most of the regions with a labour shortage, natives out-migrate because of the lack of employment possibilities. The perceived labour shortage therefore seems more to be an issue of matching problems in the labour market. Moreover, the out-migration of natives may also be explained by a labour market which already has an excess supply of labour, in certain branches or for certain qualifications. A relative labour shortage occurs when the employer cannot get the labour it needs at current market prices. In other words, to increase the wages would likely attract more labour and hence mitigate and even solve the labour shortage. To attract immigrants (to do jobs at lower wages than natives) would solve a (relative or) absolute labour shortage. If we link this back to the theoretical discussion in Sect. 3.4, we can assume that the employers stimulate a structural change when simultaneously replacing parts of the labour with capital (positive effect), close down the business and move somewhere else (positive for company/negative for the local community), or import immigrants to do the job. In the latter case there would be positive short-term effects (businesses would not move somewhere else), but in the long-term structural change might be hindered, especially in the case of less qualified immigration. Perceived labour shortages are delusive because they can lead to policy measures which, in the long-run, can contribute to the opposite effect to the one desired. Labour shortages rather indicate that the affected industries have problems of various kinds, for example in regard to poor profitability, low wages or a poor working environment. Keeping these industries alive will neither stimulate economic growth or productivity, nor will it lead to higher welfare. Nevertheless, we do acknowledge that there are

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situations where a real labour shortage occurs; for example the demand for highly skilled labour with specialised competence (e.g., STEM-­ workers) is constantly higher than the supply (Rauhut, 2002). However, this labour is predominantly demanded in cities and metropolitan regions, and not in the non-metropolitan regions we focus on in this study.

5.2  The Potential of Migrants—Educational Attainment Level and Employment Situation Human capital refers to education and training that makes human beings more productive. In short, it is about the stock of expertise accumulated by an individual. Increasing education, training and experience allows the workforce to produce more output from the same level of physical capital, and hence, they are important sources of productivity increase (Becker, 1994; Mincer, 1993), especially under the light of the described ageing of the population. In the modern knowledge-intensive economy, human capital is a pre-requisite for economic growth and progress. The higher human capital a population has, the higher productivity and economic growth (Lucas, 2015; Barro, 2001; Romer, 1990). This has a direct implication for the rural, remote, peripheral and mountainous regions studied in this volume. Regions with relatively low human capital will struggle with low productivity and low economic growth, while regions with a high human capital will perform well economically (Capello, 2016, see also Higano et al., 2002). Immigrants will not contribute to productivity increase, nor will they stimulate economic growth if they have a low human capital, but they will if they have a high human capital. However, this should not be interpreted as if a low-educated immigrant labour force cannot be useful or does not contribute to the economy—low-educated immigrant labour usually takes the jobs the natives do not want and hence mitigate the bottlenecks in production (Stark, 1991). Moreover, they also contribute to consumption within the regions (see Sect. 5.3). Within the studied areas and countries, the share of immigrants born in non-EU28 countries with a low educational attainment level is higher than for persons born in the reporting country (see Table 5.3). In parallel, the share of persons born in the reporting country with tertiary education is higher than for persons born in non-EU28 countries. This is also the case if we take a look at the analysed countries in general, where TCN are

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Table 5.3  Educational attainment levels for the studied areas and countries in 2019 (%) Less than primary, primary Tertiary education and lower secondary education Birth in reporting Birth in Birth in Birth in country non-EU28 reporting non-EU28 country country country AT Carinthia Vorarlberg Austria BG Yuzhen Tsentralen Bulgaria FI Northern and Eastern Finland Western Finland Finland DE Bavaria Germany IT Piedmont South Tyrol Italy ES Aragón Spain SE Dalarna Sweden

9.5 13.4 11.0 22.4 17.6 16.4 8.6 8.7 7.2 8.3 34.0 29.9 36.0 32.8 38.7 18.5 8.9

30.6 52.0 36.4 n/a n/a 21.5 28.2 26.0 31.6 36.2 56.7 49.0 54.4 46.5 42.5 55.6 33.8

30.6 30.0 34.1 22.5 28.0 40.6 36.9 47.1 33.4 31.1 20.5 17.2 20.7 43.8 40.6 28.5 44.0

26.4 15.3 25.3 n/a n/a 26.7 28.9 31.4 26.8 24.4 13.8 11.5 13.9 22.6 27.4 34.2 40.1

Source: Special processing of the data used by Aigner-Walder, Bauchinger, and Schomaker (2021), Amcoff (2021), Staykova-Mileva (2021), Rautiainen et  al. (2021), Weidinger et  al. (2021), Lardies-­ Bosque and del Olmo-Vicén (2021b), and Somhegyi (2021)

in all countries less educated than natives and EU migrants (for Bulgaria no data is available). Moreover, we also see a spatial difference. In cities, tertiary education is more present than in towns, suburbs and rural areas, which is the case for natives, EU citizens and TCN (Aigner-Walder, Luger, & Schomaker, 2021). Also, if we distinguish between metropolitan and non-metropolitan regions, the share of people with tertiary education is much higher in metropolitan regions (Fig. 5.4). As outlined, in the non-metropolitan case study regions analysed, TCN predominantly pick up unqualified jobs in the primary sector which are mostly jobs that natives are uninterested in (Gilli & Membretti, 2021b; Gruber, Machold, et al., 2021; Hansson et al., 2021; Kordel, Weidinger, & Güller-Frey, 2021; Krasteva et  al., 2021; Lardies-Bosque & del

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Fig. 5.4  The share of population with tertiary education by type of territory. (Source: Own elaboration)

Olmo-­Vicén, 2021a; Pöllänen et  al., 2021; Rauhut & Enlund, 2022). Thus, they may address sectors characterised by labour shortages and can fill in gaps that otherwise may lead to a lack of production or services in some sectors. In that sense, companies do not have to leave the region and positive economic effects are afforded by migration. When looking at the unemployment rates, the unemployment rates for TCN are higher or very much higher than for natives, and this is so also over time (Table 5.4). On average, for all NUTS 2 regions in 2019, the mean of the unemployment rate for natives is 6.8%, and for TCN it is 13.8%, with the statistical difference being significant. Moreover, the difference between the unemployment rate of TCN and EU citizens (which totals on average 10.1%) is also significant. Three main factors can be identified as the main causes for the higher unemployment rates of TCN in the analysed case study regions: (1) insufficient qualifications or low education to become competitive in the local/ regional labour market (Gilli & Membretti, 2021b; Gruber, Machold, et  al., 2021; Kordel, Weidinger, & Güller-Frey, 2021; Krasteva et  al., 2021; Lardies-Bosque & del Olmo-Vicén, 2021a; Mathisen & Hansson, 2022; Pöllänen et al., 2021); (2) insufficient language proficiency (Gilli & Membretti, 2021c; Gruber, Machold, et al., 2021; Kordel & Weidinger, 2021; Krasteva et al., 2021; Lardies-Bosque & del Olmo-Vicén, 2021c; Mathisen & Hansson, 2022; Rauhut et  al., 2021; Rauhut & Enlund,

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Table 5.4  Unemployment by country of birth 2009 and 2019 (%) 2009

South Austria West Austria Austria Yuzhen Tsentralen Bulgaria Northern and Eastern Finland Western Finland Finland Bavaria Germany Piedmont South Tyrol Italy Aragón Spain Norra Mellansverige Sweden

2019

Birth in reporting country

Birth in non-EU28 country

Birth in reporting country

Birth in non-EU28 country

4.6 3.5 4.7 22.8 19.5 10.5 8.9 8.1 4.5 7.1 6.1 2.2 7.5 10.1 16.1 9.1 8.0

16.5 n/a 14.9 n/a n/a n/a n/a 20.6 13.5 19.1 13.1 12.9 11.4 30.3 30.0 22.7 26.4

2.7 2.1 3.6 15.8 13.8 7.9 6.5 6.8 1.8 2.6 6.9 2.3 9.7 8.4 13.3 5.4 5.5

11.6 8.4 13.5 n/a n/a n/a 26.0a 16.8 5.3 9.6 15.5 11.8 13.9 20.7 23.0 33.7 26.9

Data for 2016

a

Source: Aigner-Walder, Bauchinger, and Schomaker (2021), Amcoff (2021), Staykova-Mileva (2021), Rautiainen et  al. (2021), Weidinger et  al. (2021), Lardies-Bosque and del Olmo-Vicén (2021b), and Somhegyi (2021)

2022); and (3) various forms of discrimination, especially towards refugees and visible minorities (Gilli & Membretti, 2021a; Kordel, Weidinger, & Güller-Frey, 2021; Krasteva, 2021; Lardies-Bosque & del Olmo-Vicén, 2021c; Machold, Dax, et al., 2021; Mathisen & Stenbacka, 2021; Pöllänen et al., 2021). Even if in some cases there is a demand for low-skilled labour, generally, the educational level has a significant influence on employment. Also for the analysed countries, we see a correlation with highest unemployment rates for the lowest educational level (primary education) and lowest unemployment rates for people with tertiary education for migrants as well as natives (Aigner-Walder, Luger, & Schomaker, 2021). The employment rates of natives (71.0% in 2019) are on average significantly higher than the employment rates of TCN (63.5% in 2019) for

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all NUTS 2 regions of the EU. Looking at the employment rates by educational attainment level, it can be seen that the employment rates of TCN are in most cases much lower than those of natives and EU citizens. Only in Italy, do TCN with primary education show higher employment rates than EU citizens and natives. Interestingly, EU citizens often show higher employment rates than natives, too, at the primary or secondary educational level. But considering tertiary education, in all countries analysed, natives have the highest employment rates. For Bulgaria no data is available for TCN (Aigner-Walder, Luger, & Schomaker, 2021). Although unemployment and employment provide indicators of the labour market performance of immigrants, this does not offer a full view of the problems and challenges immigrants encounter in the labour markets in rural, remote, peripheral and mountainous regions. To be registered as unemployed, a person needs to be on the labour market or at the disposal for any available job (Eurostat, 2021b). Hence, there is a significant hidden unemployment issue among immigrants if they potentially do not choose to be a part of the labour market. This applies particularly to female labour force participation. In some of the analysed regions, cultural differences towards work and labour market participation are reported. In the Bulgarian case study region, it was noticed that most women from Syrian and other Arabian countries have never worked. It is difficult for them to pick up jobs in the regular labour market as they are used to care for their own families (Krasteva, 2021, p. 35). The fact that women are usually the ones who are the most vivid defenders of the traditions from the home country is noted in Ostrobothnia as well, and those outside the labour market are predominantly women from outside the EU who receive different kinds of allowances from the Finnish government to take care of their children and family (Pöllänen et  al., 2021). In Ostrobothnia, 796 foreign citizens were registered as unemployed in 2021, while 1242 foreign citizens were registered as being outside the labour market (Rauhut, 2021). In Carinthia, the motivation to work of some of the immigrant groups is somewhat low (Machold, Dax, et al., 2021), but to what extent this is culturally determined is unclear. Another challenge is the mismatches seen in the local and regional labour markets. As discussed earlier in this chapter, the educational attainment level in the studied regions is significantly lower for immigrants. In addition, education and professional certificates must be validated by the host country. While labour immigrants typically have a job waiting for them upon arrival, others (e.g., refugees, family members) must pass a

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validation process. In many cases, their skills will not be recognised in the host country, which leads to a need to rebuild both education qualifications and working experience in order to access the local labour market (Laine, 2021). The consequence is a mismatch between the immigrant’s skills and the available jobs (e.g., Hansson et al., 2021; Kordel, Weidinger, & Güller-Frey, 2021; Krasteva et al., 2021; Rauhut et al., 2021). In some cases, immigrants have their skills validated just to find that they cannot get such jobs outside the bigger cities. For immigrants with higher ambitions in life than taking jobs the natives do not want, leaving the area is often the only option (Hansson et al., 2021; Lardies-Bosque & del Olmo-Vicén, 2021a; Rauhut & Enlund, 2022; Davydova-Minguet et al., 2021; Koleva, 2021; Kordel, Weidinger, & Spenger, 2021). Or they have to leave the rural, remote, peripheral and mountainous region to access the educational facilities they need to improve their human capital or skills (Davydova-Minguet et  al., 2022; Lardies-Bosque & del Olmo-­ Vicén, 2022; Machold et al., 2022; Pöllänen et al., 2021). Moreover, in many cases, the labour markets are so small in the studied regions that they need to move just to find a demanded job that fits with their qualifications. However, this is not unique for immigrants, and natives also have to do this (Kordel et al., 2022; Krasteva, 2021; Machold, Dax, et al., 2021). As a further point, discrimination might make it harder for migrants to find a job. In many of the regions analysed, the existence of discrimination against refugees is reported (Gilli & Membretti, 2021a; Gruber, Machold, et al., 2021; Kordel & Weidinger, 2021; Krasteva, 2021; Lardies-Bosque & del Olmo-Vicén, 2021b, 2021c; Machold, Dax, et al., 2021; Mathisen & Stenbacka, 2021; Pöllänen et  al., 2021). Only in Ostrobothnia in Finland have immigrants praised the friendliness and welcoming attitude of the Swedish-speaking locals (Rauhut & Enlund, 2022). To summarise, TCN generally display a lower educational attainment level than natives. Additionally, the educational attainment level in non-­ metropolitan regions is much lower than in cities and metropolitan regions. Hence, the direct potential of TCN for economic growth in non-­ metropolitan regions is not self-evident. However, they take on jobs that are not wanted by natives and in that sense can be seen of high relevance for the maintenance of companies, leading to positive effects for the region, although structural change might still be hindered. Unfortunately, the higher unemployment rates by educational attainment level suggest that their potential is not used sufficiently, which implies negative economic effects for the region and on the net transfers from natives to immigrants.

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5.3   Migrants as Consumers—The Economic Situation of Migrants Besides being economically active in the sense of participating in the labour market, migrants also contribute to the GDP as consumers. Consumption expenditure of households accounted for approximately 50% of GDP within the European Union in 2021 (Eurostat, 2022b); that is, households with their expenditures (besides investments of companies, government expenditures and exports) are the most important economic actors within the European Member States, and according to consumer sovereignty, they determine and demand the goods and services produced in (and imported to) a country, based on their needs. In general, the expenditures of households depend on their preferences and their budget. How much of the budget is saved and not spent on consumption is again dependent on income, which is positively correlated—that is, the higher the income, the more is spent on consumption. But an autonomous amount of consumption is independent of budget, as it is simply needed for staying alive. Examples are basic expenditures for food, drinks, clothes or housing (for a basic micro economic theory on the consumption of households, see, for example Mankiw & Taylor, 2020). Hence, when questioning the relevance of migrants for consumption, the most important indicator is their income. As previously mentioned, income highly depends on the educational attainment level. In all the countries analysed we see the lowest income levels for people with primary education, and the highest for those with tertiary education (Aigner-­ Walder, Luger, & Schomaker, 2021). Due to the lower educational attainment level, we would also expect lower income levels for migrants. Within the following discussion, the income situation for immigrants relative to natives is analysed based on the median equivalised net income and the at-­ risk-­of-poverty rate. The median equivalised net income1 of TCN in the surveyed countries is found in Table 5.5. With one exception, the findings in the table offer little surprise—immigrants have lower incomes than natives. In Sweden 1  To consider the impact of differences in household size and composition, the total disposable household income is equivalised. The equivalised income attributed to each member of the household is calculated by dividing the total disposable income of the household by the equivalisation factor (Eurostat, 2020). Median means that there are just as many income earners above as below the stated value. The median equivalised disposable income is the statistical measure used as the indicator of living standards in EU statistics.

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Table 5.5  Median equivalised net incomes (€) 2009 and 2019 in the analysed EU countries 2009

2019

Reporting country Non-EU28 country Reporting country Non-EU28 country Austria Bulgaria Finland Germany Italy Spain Sweden

21,595 2844 21,239 18,880 16,298 15,785 20,982

15,075 3061 14,775 13,483 11,700 9832 12,576

27,7494 4285 25,187 23,725 18,098 15,929 25,962

19,090 4899 18,882 19,293 12,363 8954 14,108

Source: Aigner-Walder, Bauchinger, and Schomaker (2021), Amcoff (2021), Staykova-Mileva (2021), Rautiainen et  al. (2021), Weidinger et  al. (2021), Lardies-Bosque and del Olmo-Vicén (2021b), and Somhegyi (2021)

and Spain, the median income of TCN is only around 55% of the income of natives. The exception is Bulgaria where the median equivalised net income for immigrants is higher than for natives in 2009 and 2019. Moreover, TCN have a lower income than EU migrants in all analysed countries, and in Germany the income of EU28 migrants is even higher than that of natives (Aigner-Walder, Luger, & Schomaker, 2021). However, Table 5.5 only contains information at the national level. As statistical data is scarce at regional level, we must rely on a qualitative assessment of immigrants and stakeholders in the studied rural, remote, peripheral and mountainous regions. In all the studied case study regions, the findings suggest that immigrants have lower to significantly lower incomes than natives (Gilli & Membretti, 2021c; Hansson et al., 2021; Koleva, 2021; Lardies-Bosque & del Olmo-Vicén, 2021d; Machold, Bauchinger, et al., 2021; Rauhut, 2021). It is also worth noting that many of the analysed case study regions report a very high welfare dependency for immigrants (Gilli & Membretti, 2021c; Gruber, Lobnig, & Kathrin Zupan, 2021; Machold, Bauchinger, et al., 2021; Mathisen & Stenbacka, 2021; Rauhut, 2021). This is not a surprising finding, and with higher unemployment rates than natives, high shares of people outside the labour market and lower incomes, a higher welfare dependency among immigrants relative to natives is to be expected. One indicator which already includes social transfers is the at-risk-of-­ poverty rate. This is the share of people with an equivalised disposable

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Table 5.6  At-risk-of-poverty rates during 2009 and 2019  in the analysed EU countries (%) 2009

2019

Reporting country Non-EU28 country Reporting country Non-EU28 country Austria Bulgaria Finland Germany Italy Spain Sweden

11.8 21.2 13.9 15.5 16.5 16.2 12.6

33.9 23.1 40.0 28.7 32.4 41.0 47.9

10.2 21.6 11.7 15.4 18.1 16.4 12.3

29.5 20.0 23.5 22.7 34.8 50.1 54.0

Source: Aigner-Walder, Bauchinger, and Schomaker (2021), Amcoff (2021), Staykova-Mileva (2021), Rautiainen et  al. (2021), Weidinger et  al. (2021), Lardies-Bosque and del Olmo-Vicén (2021b), and Somhegyi (2021)

income (after social transfer) below the at-risk-of-poverty threshold, which is set at 60% of the national median equivalised disposable income after social transfers. This indicator does not measure wealth or poverty but low income in comparison to other residents in that country, which does not necessarily imply a low standard of living (Eurostat, 2021c). Looking at the data (Table 5.6), we get a similar picture compared to the income of TCN. Immigrants have a higher at-risk-of-poverty rate, being highest in Sweden and Spain. The exception again is Bulgaria. The at-risk-of-poverty rate for immigrants is marginally higher than for natives in 2009, but in 2019 the opposite situation exists. Additionally, the at-risk-of-poverty rate for TCN is in all countries higher than for EU28 migrants, and in Germany, EU migrants are at a lower risk of poverty than natives (Aigner-Walder, Luger, & Schomaker, 2021). However, there is again only national data available for analysis. To sum up, the economic situation of immigrants is worse compared to natives. This does not mean that they do not consume products and services, but it might mean that the relevance of their consumption for the economy might be negligible compared to natives, and there is not much money left for saving or investments after the consumption of basic goods and services needed for living. But this basic consumption is usually produced locally (e.g., housing, medical or personal care services) and is therefore a factor that should be considered.

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Fig. 5.5  Primary income by type of territory. (Source: Own elaboration)

From a theoretical point of view, immigration might lead to negative effects on the income of native workers (Borjas, 2001; Ruist, 2022, C.f. Docquier et al., 2017). In such a case, this could also have negative effects on (local) consumption. But positive effects on the wages of capital owners are expected. We do not have any data on wages to analyse the potential effects for various groups. But as the income of TCN is lower, we would (1) expect a higher marginal propensity to consume, and (2) luxury goods and investments goods, which might primarily be consumed by natives, have a higher probability to be imported, with lower local economic effects. In general, we find an existing income differential between metropolitan and non-metropolitan regions (see Fig. 5.5).

5.4   Migrants as Innovators?—A Way Out Vicious Circle of Underdevelopment

of a Potential

As already mentioned, the studied regions report a labour shortage, especially for unqualified labour in the fields of agriculture and fishing, manual manufacturing, and the service sector (Gilli & Membretti, 2021b; Gruber, Machold, et al., 2021; Hansson et al., 2021; Kordel, Weidinger, & Güller-­ Frey, 2021; Krasteva et  al., 2021; Lardies-Bosque & del Olmo-Vicén, 2021a; Rauhut et al., 2021). However, a relatively small number of labour immigrants in low-productive and labour-intensive branches can keep the production going and hence save jobs for a number of natives in the same

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region. If the production keeps going, the government will still receive tax revenue from employees and companies, and consumption will be upheld. When seen from this perspective, immigrant labour in low-productive and labour-intensive branches is valuable for these rural and peripheral host regions. While the TCN contribute to increase the relative competitive advantages for the region towards other regions hosting traditional labour-­ intensive production, they also hinder structural change. A structural change to increase, for example, productivity, savings, consumption and incomes would also increase the relative competitive advantage. If it is cheaper for companies to employ low-productive immigrant labour in labour-intensive jobs to mitigate a labour shortage and keep some branches alive, then the structural change in the economy will be slowed down. From a short-term perspective, these developments might be positive for the involved companies and regions, and eventually, from a macroeconomic perspective (the net transfers between immigrants and natives). However, from a long-term perspective, the slowed-down structural change will have negative economic effects which lead to lower productivity, lower profits, lower investments, etc. In the worst case, the region may get trapped in a vicious circle of underdevelopment (Rauhut & Rauhut Kompaniets, 2018; see also Sect. 3.4). However, innovation and entrepreneurship play a key role for regional development, and immigrant entrepreneurship as well as innovations by immigrants can break a negative development. This is the case both when it comes to goods and also for services. In cases where produced services are highly productive and capital-intensive, then they can stimulate regional economic growth and development (Nijkamp & Poot, 2012). To be self-employed or an entrepreneur is an alternative to being employed. The findings from the rural, remote, peripheral and mountainous regions analysed in this study display a quite fragmented result. While immigrants are hesitant to become self-employed and are everything but entrepreneurial in Ostrobothnia in Finland (Rauhut, 2021) and Italy (Gilli & Membretti, 2021b), in the other studied regions, self-employment seems to offer a route out of unemployment. In these cases, self-­employment is usually related to low-productive and labour-intensive jobs in the service sector (Gruber, Machold, et  al., 2021; Hansson et  al., 2021; Kordel, Weidinger, & Güller-Frey, 2021; Krasteva et al., 2021; Lardies-­Bosque & del Olmo-Vicén, 2021a; Rauhut et al., 2021). Especially in rural, remote, peripheral and mountainous regions, the demand for the production of

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services is lower compared to cities (Broström & Rauhut, 2017), although, for example, restaurants and cafés are common (e.g., Gruber, Machold, et  al., 2021; Lardies-Bosque & del Olmo-Vicén, 2021a). Theoretically, without a sufficient demand for these services, profitability will be low, and there is a risk for companies to become financially insolvent (Begg et al., 1987; Wonnacott & Wonnacott, 1986; Hollensen, 2020). Overall, the level of immigrant entrepreneurship is low in many of the case study regions, and innovations by immigrants are scarce. In the studied Bulgarian region, according to the empirical results, the immigrant companies are not economically sustainable (Krasteva et al., 2021), which is something which indicates low profitability.2 Moreover, the growth of businesses is lower in non-metropolitan regions compared to metropolitan regions (Table  5.7). As the market in non-metropolitan regions is smaller than in metropolitan ones, this is not a surprising finding. The widely held notion that immigrants are more entrepreneurial than natives and that they start businesses in non-metropolitan regions finds no support in our empirical material. Rather, it is the case that a higher share of the non-EU28 population is related to fewer company start-ups Table 5.7  Net business population growth in 2018 Increase (%) Bulgaria Sofia Non-metropolitan regions in Bulgaria Spain Madrid Non-metropolitan regions in Spain Italy Milano Torino Non-metropolitan regions in Italy Austria Wien Non-metropolitan regions in Austria

1.86 2.39 1.26 2.84 2.85 2.68 0.18 1.09 0.38 −0.13 −0.5 −0.4 −0.49

Source: Eurostat, Table [MET_BD_HGN2__custom_2868444]

2  Also in Norway, immigrant companies are more likely to go bankrupt than companies started by natives (Blumenthal & Lund, 2021).

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Fig. 5.6  Company start-ups and share of non-EU28 population in non-­ metropolitan regions. (Source: Own elaboration)

(Fig. 5.6). The determination coefficient,3 R2, is 0.263. The corresponding value for metropolitan regions is R2 = 0.054 and the relationship is negative. It is worth noting that analysing the company start-ups by educational level among the share of non-EU28 population did not produce any high determination coefficients, but the negative correlation prevails between the company start-ups in metropolitan areas by the share of non­EU28 population with primary education (R2 = 0.206). However, a positive correlation exists between the company start-ups in metropolitan regions and the share of non-EU28 population with tertiary education, with an R2 = 0.202. Again, it seems as if the low-skilled non-EU28 immigrants have difficulties in finding employment also as self-employed workers. Kordel and Weidinger (2018) argued that immigrants do not start up companies in rural areas as many stakeholders have hoped for, and they do not establish themselves in the labour market. We also find no positive 3  The determination coefficient measures the proportion of the total variation in y that is accounted for by the variation in the regressor x (Greene, 2020). The higher the value in a bivariate regression, the more of y can be explained by x (Ramanathan, 1995).

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correlation of TCN with company start-ups in our data. Moreover, Andersson et al. (2021) conclude that people from the Middle East will leave non-employment for entrepreneurship if many local members of the local diaspora are business owners. Seen in the perspective analysed in this volume and differenced between metropolitan and non-metropolitan regions, one possible interpretation could be that a critical mass is not reached in non-metropolitan regions. An innovation refers to the commercial applications of new technology, new material, new methods or new sources of energy. An innovation should not be mixed up with introducing existing products and services to an existing or new market (Flikkema et  al., 2007; Rydvalova & Skala, 2021). Immigrants can contribute to innovations as innovators when they come up with new and unique ideas related to products or processes. However, there is an inherent confusion between, on the one hand, innovations, and, on the other hand, market development and product development. Starting a restaurant, takeaway services or home delivery services, etc., is not about innovation but about market and product development (see Kotler et al., 2020). As discussed earlier in this chapter, most immigrant labour in regions beyond the cities are occupied in low-productive and labour-intensive work in the primary sector, in manual manufacturing or unqualified service jobs, both as employed and self-employed workers. The empirical data collected from these regions does not contain any evidence that immigrants are innovative and stimulate innovations. Nevertheless, immigrants may be good at identifying and profiting from market development and product development. In many of the surveyed regions, immigrants have brought food products and styles from their home countries with them, which are sold in restaurants or supermarkets, and there is evidence that immigrants are good at starting up takeaway services or home delivery services in the surveyed regions (Gilli & Membretti, 2021a; Gruber, Machold, et al., 2021; Hansson et al., 2021; Kordel, Weidinger, & Güller-Frey, 2021; Krasteva, 2021; Lardies-Bosque & del Olmo-Vicén, 2021a; Rauhut et al., 2021). Moreover, TCN offer a potential for the cultural enrichment of rural, peripheral, remote and mountainous areas. Culture is expected to have positive effects on innovations and economic development (for an exemplary discussion, see Kostis, 2021). In many of the studied regions, as well as immigrants having brought their foods with them, they exercise their own culture and traditions. In some cases, immigrants have opened

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restaurants or cafés, and in other cases they have opened mini- or supermarkets with their own traditional foods (Gruber, Lobnig, & Kathrin Zupan, 2021; Gruber, Machold, et  al., 2021; Koleva, 2021; Kordel & Weidinger, 2021; Pöllänen et  al., 2021). But, in the Finnish region of Ostrobothnia, many of the local stakeholders had hoped that the immigrants would enrich culture and cultural activities more than actually had taken place (Rauhut, 2021). Lastly, it should be mentioned that TCN are working in innovative branches. For example, in Sweden more than 10% of the people employed in the information and communication sector (known as an innovation-­ driven sector) are neither Swedish nor European, but from the rest of the world. Also, in Austria around 7% of the people working for ICT manufacturing or services are TCN (Aigner-Walder, Luger, & Schomaker, 2021).

5.5  Overall Economic Effects of Migration— TCN and Economic Growth Commonly, it is assumed that the economic consequences of immigration in rural, peripheral, remote and mountainous areas will be about the same as in metropolitan regions or at the national level, but as we will show, this is a bold assumption. Low-productive and labour-intensive jobs in agriculture, manual manufacturing and services for unskilled labour with low human capital and a high share of seasonal and shorter temporal contracts do not stimulate productivity and economic growth. On the contrary, these kinds of jobs (as soon as they go beyond the delivery of basic goods or services) obstruct the structural change in the economy. Consequently, a lower GDP per capita can be expected for those regions compared to regions where productivity gains are stimulated and labour displays high human capital. Scrutinising these deliberations, it can be assumed that the GDP and economic growth in the studied rural, peripheral, remote and mountainous regions will be lower than for the economic and financial centres in the same countries. Our data on Europe supports this, and significant differences exist between the analysed spatial categories, where the GDP in purchasing power standards (PPS) per inhabitant is much lower in non-metropolitan regions (Fig.  5.7). Considering the more in-depth studied non-metropolitan regions, the GDP per capita is in all regions lower to significantly lower than in the economic centres (Table 5.8).

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Fig. 5.7  GDP in PPS per inhabitant by regional category. (Source: Own elaboration)

Moreover, taking data for 2019, GDP growth rates display a significant difference depending on the type of territory taken into consideration, with growth rates being lower in non-metropolitan regions (see Fig. 5.8). Interestingly, we do not see such striking results for our selected case study regions (see Table 5.8), and in Austria, Finland, Germany and Italy growth rates in specific rural areas were higher than in the capital. A more detailed look at various economic indicators reveals several interesting findings on the effects of immigration. First, the unemployment rate in non-metropolitan regions is pushed upwards by the share of non-EU28 population with primary education as their highest educational attainment level. The determination coefficient, R2, is 0.387 (Fig. 5.9). In metropolitan regions, R2 = 0.201. The coefficient for the unemployment rate in both metropolitan and non-metropolitan regions for the share of non-EU28 population with tertiary education as highest educational attainment level was negative, with determination coefficients of R2 = 0.191 and R2 = 0.120 respectively. Second, the higher the share of non-EU28 population in non-­ metropolitan regions with primary education as the highest educational attainment level, the lower the employment rate in non-metropolitan regions. The determination coefficient, R2, is 0.342 (Fig. 5.10). In metropolitan regions, R2 is 0.210. The coefficients display a positive

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Table 5.8  GDP per capita and real growth rates in national economic centres and rural, remote, peripheral and mountainous regions studied in selected countries (2019)

AT

BG

FI

DE

IT

ES

SE

AT13 Vienna AT21 Carinthia AT211 Klagenfurt-Villach AT34 Vorarlberg AT341 Bludenz-Bregenzer Wald BG34 Sofia BG42 Yuzhen Tsentralen BG422 Haskovo FI1B Helsinki-Uusimaa FI19 Western Finland FI195 Ostrobothnia FI1D Eastern and Northern Finland FI1D3 North Karelia DE60 Hamburg DE21 Oberbayern DE215 Berchtesgadener Land DE21D Garmisch-Partenkirchen ITC4C Milano ITC1 Piemonte ITC11 Torino ITH10 South Tyrol ES30 Comunidad de Madrid ES24 Aragón ES241 Huesca SE10 Stockholm SE31 Norra Mellansverige SE312 Dalarna

GDP per capita (€)

Real growth rate (%)

52,600 38,300 44,000 47,800 52,000 14,800 6200 4600 56,800 38,200 40,900 36,000 34,000 67,300 60,600 35,400 31,900 55,600 31,900 33,600 48,400 36,000 28,800 28,400 64,100 37,100 37,800

3.0 2.8 2.9 2.5 5.9 14.2 9.4 4.6 1.4 2.0 1.6 1.6 0.8 4.4 3.9 6.2 5.4 1.0 0.3 −0.8 2.9 4.4 3.3 3.6 2.4 −0.3 −0.1

Source: Aigner-Walder, Luger, and Schomaker (2021), Eurostat (2022c)

relationship for the highly skilled in both metropolitan and non-metropolitan regions, with determination coefficients of 0.114 and 0.151. The unskilled labour seems to generate an excess supply of labour, which materialises in a high unemployment rate and low employment rates. For the variable of GDP per capita in purchasing power standards in metropolitan regions, the determination coefficient only displayed a positive correlation of R2 = 0.126 with the share of non-EU28 population in metropolitan areas, which is a weak correlation. The corresponding

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Fig. 5.8  Annual percentage change of gross value added (GVA). (Source: Own elaboration)

Fig. 5.9  Unemployment rate by share of non-EU28 population with primary education in non-metropolitan regions. (Source: Own elaboration)

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Fig. 5.10  Employment rate by share of non-EU28 population with primary education in non-metropolitan regions. (Source: Own elaboration)

determination coefficient for non-metropolitan areas was positive but even weaker, and so were the determination coefficients explaining the GDP per capita in purchasing power standards by both primary and tertiary educational levels in both metropolitan and non-metropolitan regions. While the share of non-EU28 population with tertiary education in the metropolitan regions has a clear positive impact on the growth of the gross value added, R2=0.355 (Fig. 5.11), the share of non-EU28 population with tertiary education in the non-metropolitan regions does not have the same effect, and in that case, the correlation is close to nothing, R2  =  0.016. Considering the positive correlation between migrants with tertiary education and economic growth, we can question whether highly qualified TCN are the source of economic growth or whether economic growth is the source for highly qualified immigration to these areas (chicken-egg problem). If the highly skilled non-EU immigrants had a positive impact on economic growth in the metropolitan regions, the low-skilled had the opposite effect. The share of non-EU28 population with primary education in the metropolitan regions has a clear negative impact on economic growth, R2 = 0.311 (Fig. 5.12). The impact of low-skilled non-EU28 immigrants on economic growth in non-metropolitan regions is close to nothing, R2 = 0.019.

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Fig. 5.11  Economic growth and the share of non-EU28 population with tertiary education in metropolitan regions. (Source: Own elaboration)

Fig. 5.12  Economic growth and the share of non-EU28 population with primary education in metropolitan regions. (Source: Own elaboration)

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That highly skilled non-EU28 immigrants contribute to the knowledge-­ intensive production of goods and services in the metropolitan regions (and hence to productivity increases) will leave its mark on the economic growth. A secondary effect may also be that the metropolitan regions end up in a positive circle of regional development, while the non-­metropolitan regions run the risk of getting caught in a vicious circle of underdevelopment, as explained above. For the primary income, we could not find any significant impact in metropolitan nor non-metropolitan regions by the non-EU28 population as a whole or by educational attainment levels.

5.6  Do Regions Beyond the Cities Gain Nevertheless?—The Relevance of Fiscal Regulations A key question on the economic effects of migration to rural areas might also be who carries the costs of migration, in the sense of integration costs and social transfers to migrants. One could argue that if these costs are carried by higher government levels (regional states or even the national government), the potential economic surplus of migrants for regions would be much higher. Or in other words, if local and regional authorities have to carry these costs on their own, the net effect of migration might be negative. The fiscal effect refers to the difference between the costs of publicly financed services utilised by immigrants and the tax revenues from immigrants. These costs are subject to considerable debate and vary over time and between countries. The fiscal effect also differs in short- or long-­term perspectives, and on direct and indirect effects as well as externalities (Bodvarsson & van den Berg, 2013). Moreover, the fiscal impact of immigration is also affected by the age of the immigrants, the level of human capital, cause of stay (e.g., labour migrant, refugee or asylum-seeker, tied mover or students), the construction of the tax system and the generosity of the welfare system (Bansak et al., 2021; Ruist, 2022). Following the argument we introduced in Chap. 3; whether the labour that immigrants offer to the labour market is demanded or not poses a significant impact on the fiscal effect, as well as to what extent the immigrant is allowed to work or not. In the latter case, the immigrant has to be, in one way or another, provided for or at least until the immigrant can provide for him/ herself (see Fig. 5.13).

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Fig. 5.13  Fiscal effects in theory. (Source: Own elaboration)

However, immigrants supplying the labour market with labour without any matching demand will also have a negative impact on the fiscal effects. An example of this is when unskilled cheap labour has been imported to keep stagnating branches alive. After these branches have been outcompeted, this labour is no longer in demand in the labour market (Lundh & Ohlsson, 1994). In the short-term perspective, these immigrants contributed to a favourable fiscal effect of immigration, but in a long-term perspective, due to a higher welfare dependency, the fiscal effect might be negative. Estimating the fiscal effects of immigration is a challenging exercise which is based on many assumptions. Hence, the results should be considered indicative rather than precise. Bansak et  al. (2021, p.  296) conclude that: [a]lthough estimates vary widely, some patterns do emerge. First, in most countries the fiscal impact of immigration appears to be rather small as a share of overall economic output. Studies that look at the impact in a given year—the static accounting method—typically find an impact between -1 to +1 percent of GDP. Second, immigrants’ labour market success affects their fiscal impact. In countries with relatively skilled immigrants admitted on the basis of employment /…/ immigrants add to government coffers. Countries

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with sizeable humanitarian immigrant populations experience smaller fiscal gains or even fiscal losses from immigration. Third, immigrants age at arrival and their education matters /…/. Fourth, educational investments in immigrant children and assimilation can help alleviate fiscal imbalances, particularly for government-funded retirement benefit programs, in destination countries.

Figure 5.14 illustrates the age-specific net contributions to the Danish public finances in 2018 by origin (natives, Western origin, non-Western origin). While the immigrants of Western origin are generally labour immigrants, the immigrants of non-Western origin are mainly refugees and asylum-seekers. The figure illuminates the importance of labour market success. However, it also illuminates the difference between immigrants who are allowed to work and offering demanded labour, and those who are not allowed to work or offering labour with no demand. So far, the discussion has focused on the aggregate level, that is, the fiscal effects immigration has on the national level. However, immigrants are not evenly distributed geographically in a country, nor is the demand for the labour that immigrants supply to the market. In the case of the US, the fiscal effects at state level illustrate how much they can vary within one country: ‘Immigrant households have widely diverse impacts on state 300 200 100 0 –100

0

10

20

30

40

50

60

70

80

90

100

–200 –300 –400 –500 –600 Danish origin

Non-western origin

Western origin

Fig. 5.14  Age-specific net contributions to the Danish public finances by origin in 2018 (thousands DKK). (Source: Danish Ministry of Finance (2018))

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balance sheets, ranging from a net fiscal shortage of $10,000 per first-­ generation immigrant household in Minnesota to a net fiscal benefit of $7,350 in Alaska’ (Bansak et al., 2021, p. 287). Moreover, most countries apply different forms of dispersal policies for refugees and asylum-seekers, which distributes them based on other reasons than the demand for labour. In some countries, the local authorities finance the produced welfare by local taxes, yet in other countries regional or federal authorities exert this right. The outcome of this is that the fiscal effects of immigration at a sub-­ national level can display a significant variation within the countries concerned. In Sweden (a country with a high share of humanitarian immigrants), the government covers the costs for refugees and asylum-seekers for the two first years. After that, the municipalities have to cover the costs for immigrant integration themselves. While a majority of the municipalities in and around metropolitan areas state that their costs are significantly higher than the funding received from the government, rural municipalities state the opposite (Riksrevisionen, 2017). One reason for this may be that not all the services provided in the cities are provided in sparsely populated rural areas, and private actors who produce the services do not operate in areas with insufficient demand as it would be unprofitable for them (Gruber & Rauhut, 2023). Only 38% of all households in Sweden 2020 receiving social assistance are native, while 62% are of foreign origin (Socialstyrelsen, 2022). Since social assistance is financed by the municipal tax in Sweden, such a high share of immigrants living on social assistance has a significant impact on the fiscal effects at the local level. In many of the regions in the surveyed countries, much of the responsibility for integrating refugees lies with the local or regional authorities, whose resources are limited. In many cases, resources are spent on housing, education and labour market training for persons who will most likely leave these rural, remote, peripheral and mountainous areas as soon as they can. When the refugees leave the studied areas for bigger cities, the cities will profit from the investments that many of the local and regional authorities in rural, remote, peripheral and mountainous areas have made in the refugees. Unless, the local and regional authorities are renumerated by the state, the local and regional levels will be drained of resources over time, which will increase the urban-rural gap. The fiscal effects will also be negative for them, but to what extent this is a desired development is a political question.

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5.7  Summary Europe faces an ageing population, with the consequence of an ageing and declining workforce being a challenge for economic development. These developments are more pronounced in non-metropolitan areas, making migrants a relevant source of labour. However, as we see, migrants might not have the right qualifications or face other challenges considering labour market integration. They are characterised by a lower educational level, lower employment rates by educational level and higher unemployment rates. Hence, their potential to overcome shortages in the labour market does not seem to be fully used. Positive economic effects of migrants are nonetheless to be expected. One reason is that low-skilled labour is also demanded, and in such cases, migration helps to overcome supply shortages. Moreover, due to their (local) consumption and the fact that part of it will be independent of income (autonomous consumption; e.g., food and housing), economic activity is positively affected. However, with the bigger part of consumption being dependent on income, the demand effect of migrants might be lower than that of natives, due to an income level below average and a poverty rate above average. Although we cannot find any empirical evidence in the regions beyond the cities to support the claim that immigrants are more entrepreneurial, our qualitative data displays that immigrants are good at market and product development. However, this should not be confused with being innovative. For immigrants, self-employment and entrepreneurship appear to be closely related to the failure of establishing themselves in the labour markets. Moreover, the empirical findings also indicate that the unemployment rate in metropolitan regions will increase when the share of low-educated immigrants increases, but in non-metropolitan regions the correlation is insignificant. The employment rate in non-metropolitan regions will decrease when the share of immigrants increases, and in metropolitan regions the correlation is again insignificant. In regard to the correlation between high-skilled immigrants and unemployment, unemployment only produces very low determination coefficients. These results are the same for high-skilled immigrants in both metropolitan and non-­ metropolitan regions. One explanation for this finding may be as simple as that highly skilled immigrants are simply too few in number to produce any significant effect. The effects of immigration on economic growth

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display different outcomes in metropolitan and non-metropolitan regions. In metropolitan regions, the economic growth will be lower when the share of low-educated immigrants increases, and it will increase when the share of high-educated immigrants increases. For non-metropolitan regions the correlation between economic growth and immigrants, both high- and low-educated, is insignificant. Lastly, considering a potential migration surplus, the costs of migration also have to be taken into account. Besides costs for integration, also public services and the social aid provided to migrants have to be considered. Whether a region beyond the cities profits from migration also depends on which government level pays for these costs. As local and regional governments are mainly responsible for this, the potential overall positive effects of migration might be heavily reduced.

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Koleva, C. (2021). Bulgaria. In A. Membretti (Ed.), 13 quantitative briefing on the case studies (pp. 68–92). MATILDE deliverable 5.2. https://doi.org/10.5281/ zenodo.5526040 Kordel, K., Weidinger, T., & Spenger, D. (2022). Germany: Bavaria. In M. Gilli & A.  Membretti (Eds.), 13 action-research reports (pp.  145–174). MATILDE deliverable 5.3. https://doi.org/10.5281/zenodo.6372113 Kordel, S., & Weidinger, S. (2021). Germany. In J.  Laine (Ed.), Ten country reports on qualitative impacts of TCNs (pp. 73–91). MATILDE deliverable 3.3. https://doi.org/10.5281/zenodo.4726645 Kordel, S., & Weidinger, T. (2018). Current processes in immigration to European peripheries: Status quo, implications and development strategies. In S. Kordel, T. Weidinger, & I. Jelen (Eds.), Processes of immigration in rural Europe: The status quo, implications and development strategies (pp. xv–xxx). Cambridge Scholars. Kordel, S., Weidinger, T., & Güller-Frey, A. (2021). Germany. In M. L. Caputo et al. (Eds.), Ten country reports on economic impact (pp. 130–160). MATILDE deliverable 4.3. https://doi.org/10.5281/zenodo.5017813 Kordel, S., Weidinger, T., & Spenger, D. (2021). Germany. In A.  Membretti (Ed.), 13 quantitative briefing on the case studies (pp.  146–181). MATILDE deliverable 5.2. https://doi.org/10.5281/zenodo.5526040 Kostis, P. C. (2021). Culture, innovation, and economic development. Journal of Innovation and Entrepreneurship, 10, 22. https://doi.org/10.1186/s13731-­ 021-­00163-­7 Kotler, P., Armstrong, G., Harris, L. C., & He, H. (2020). Principles of marketing. Pearson. Krasteva, A. (2021). Bulgaria. In J. Laine (Ed.), Ten country reports on qualitative impacts of TCNs (pp.  27–48). MATILDE deliverable 3.3. https://doi. org/10.5281/zenodo.4726645 Krasteva, A., Koleva, C., & Ninova, V. (2021). Bulgaria. In M.L. Caputo et al. (Eds.), Ten country reports on economic impact (pp. 55-92). MATILDE deliverable 4.3. https://doi.org/10.5281/zenodo.5017813 Laine, J. (2021). Comparative report and social innovation practices. MATILDE deliverable, 3, 4. https://doi.org/10.5281/zenodo.5017793 Lardies-Bosque, R., & del Olmo-Vicén, N. (2021a). Spain. In M. L. Caputo et al. (Eds.), Ten country reports on economic impact (pp.  219–249). MATILDE deliverable 4.3. https://doi.org/10.5281/zenodo.5017813 Lardies-Bosque, R., & del Olmo-Vicén, N. (2021b). Spain. In J.  Laine & D.  Rauhut (Eds.), Ten statistical briefings on immigration’s social impacts (pp.  113–135). MATILDE deliverable 3.2. https://doi.org/10.5281/ zenodo.4726634

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Lardies-Bosque, R., & del Olmo-Vicén, N. (2021c). Spain. In J. Laine (Ed.), Ten country reports on qualitative impacts of TCNs (pp.  126–144). MATILDE deliverable 3.3. https://doi.org/10.5281/zenodo.4726645 Lardies-Bosque, R., & del Olmo-Vicén, N. (2021d). Spain. In A.  Membretti (Ed.), 13 quantitative briefing on the case studies (pp.  244–288). MATILDE deliverable 5.2. https://doi.org/10.5281/zenodo.5526040 Lardies-Bosque, R., & del Olmo-Vicén, N. (2022). Spain: Aragón. In M. Gilli & A.  Membretti (Eds.), 13 action-research reports (pp.  261–290). MATILDE deliverable 5.3. https://doi.org/10.5281/zenodo.6372113 Lee, R. (2016). Macroeconomics, aging and growth. In J. Piggott & A. Woodland (Eds.), Handbook of the economics of population aging (pp. 59–118). Elsevier. Lindh, T., & Malmberg, B. (1999). Age structure effects and growth in the OECD: 1950–1990. Journal of Population Economics, 12, 431–449. Lucas, R. E., Jr. (2015). Human capital and growth. American Economic Review, 105(5), 85–88. Lundh, C., & Ohlsson, R. (1994). Immigration and Economic Change. In T. Bengtsson (Ed.), Population, Economy and Welfare in Sweden (pp. 87–107). Springer. Machold, I., Bauchinger, L., Dax, T., & Manahl, C. (2021). Austria: Vorarlberg. In A. Membretti (Ed.), 13 quantitative briefing on the case studies (pp. 36–67). MATILDE deliverable 5.2. https://doi.org/10.5281/zenodo.5526040 Machold, I., Bauchinger, L., Dax, T., Manahl, C., & Hörl, M. (2022). Austria: Vorarlberg. In M.  Gilli & A.  Membretti (Eds.), 13 action-research reports (pp.  9–37). MATILDE deliverable 5.3. https://doi.org/10.5281/zenodo. 6372113 Machold, I., Dax, T., Bauchinger, L., Gruber, M., et  al. (2021). Austria. In J. Laine (Ed.), Ten country reports on qualitative impacts of TCNs (pp. 5–26). MATILDE deliverable 3.3. https://doi.org/10.5281/zenodo.4726645 Maestas, N., Mullen, K. J., & Powell, D. (2016). The effect of population aging on economic growth, the labor force and productivity. NBER Working Paper 22452. Retrieved October 17, 2022, from http://www.nber.org/papers/w22452 Mankiw, G. N., & Taylor, M. P. (2020). Microeconomics (5th ed.). Cengage Learning. Martins, J. O., et al. (2005). The impact of ageing on demand, factor markets and growth (OECD Economics Department Working Papers, No. 420). OECD Publishing. Mathisen, T., & Hansson, U. (2022). Sweden: Dalarna. In M. Gilli & A. Membretti (Eds.), 13 action-research reports (pp.  291–316). MATILDE deliverable 5.3. https://doi.org/10.5281/zenodo.6372113 Mathisen, T., & Stenbacka, S. (2021). Sweden. In J. Laine (Ed.), Ten country reports on qualitative impacts of TCNs (pp.  145–162). MATILDE deliverable 3.3. https://doi.org/10.5281/zenodo.4726645 Mincer, J. (1993). Studies in human capital. Edward Elgar.

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Nijkamp, P., & Poot, J. (2012). Migration impact assessment: A state of the art. In P.  Nijkamp, J.  Poot, & M.  Sahin (Eds.), Migration impact assessment (pp. 3–62). Edward Elgar. OECD. (2011). OECD economic outlook. Volume 2011/1. Paris. OECD. (2022). The Contribution of Migration to Regional Development, OECD Regional Development Studies, OECD Publishing, Paris, https://doi.org/ 10.1787/57046df4-en. Pöllänen, P., Havukainen, L., & Rauhut, D. (2021). Finland. In J. Laine (Ed.), Ten country reports on qualitative impacts of TCNs (pp.  49–72). MATILDE deliverable 3.3. https://doi.org/10.5281/zenodo.4726645 Prskawetz, A., & Lindh, T. (2007). The Relationship between demographic change and economic growth in the EU (Research Report 32). Vienna Institute of Demography. Ramanathan, R. (1995). Introductory econometrics. Dryden Press. Rauhut, D. (2002). Arbetskraftsbrist och arbetsinvandring – hot eller möjlighet för ekonomisk tillväxt? Institutet för tillväxtpolitiska studier, Rapport A2002:010. Rauhut, D. (2021). Finland: Ostrobothnia. In A. Membretti (Ed.), 13 quantitative briefing on the case studies (pp.  115–145). MATILDE deliverable 5.2. https://doi.org/10.5281/zenodo.5526040 Rauhut, D., & Enlund, M. (2022). Finland: Ostrobothnia. In M. Gilli, & A. Membretti (Eds.), 13 Action-Research Reports (pp. 93–117). MATILDE deliverable 5.3. https://doi.org/10.5281/zenodo.6372113 Rauhut, D., Pöllänen, P., Havukainen, L., Laine, J., & Davydova-Minguet, O. (2021). Finland. In M.  L. Caputo et  al. (Eds.), Ten country reports on economic impact (pp.  93–129). MATILDE deliverable 4.3. https://doi. org/10.5281/zenodo.5017813 Rauhut, D., & Rauhut Kompaniets, O. (2018). The impact of immigrant entrepreneurship on regional development in Western Sweden. Romanian Journal of Regional Science, 12(1), 18–42. Rautiainen, T., Rauhut, D., & Havukainen, L. (2021). Finland. In J.  Laine & D.  Rauhut (Eds.), Ten statistical briefings on immigration’s social impacts (pp.  39–60). MATILDE deliverable 3.2. https://doi.org/10.5281/ zenodo.4726634 Riksrevisionen. (2017). Kommunersättningar för migration och integration – ett ogenomtänkt system. Rapport RIR 2017:10. Stockholm. Romer, P. M. (1990). Human capital and growth: Theory and evidence. Carnegie-­ Rochester Conference Series on Public Policy, 32, 251–286. Rostow, W. W. (1998). The great population spike and after. Oxford University Press. Ruist, J. (2022). Causes and consequences of global migration. Anthem press. Rydvalova, P., & Skala, M. (2021). Innovation and innovation partnership. In M. Zizka & P. Rydvalova (Eds.), Innovation and performance drivers of business

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clusters. Science, technology and innovation studies. Springer. https://doi. org/10.1007/978-3-030-79907-6_4. Sheiner, L. (2014). The determinants of the macroeconomic implications of aging. American Economic Review, 104(5), 218–223. https://doi.org/10.1257/ aer.104.5.218 Socialstyrelsen. (2022). Statistik om ekonomiskt bistånd 2021. Tabellbilaga, Tabell 3. Accessed on 16 October 2022 at https://www.socialstyrelsen.se/statistik-ochdata/statistik/alla-statistikamnen/ekonomiskt-bistand/ Somhegyi, T. F. (2021). Italy. In J. Laine & D. Rauhut (Eds.), Ten statistical briefings on immigration’s social impacts (pp. 78–96). MATILDE deliverable 3.2. https://doi.org/10.5281/zenodo.4726634 Stark, O. (1991). The migration of labor. Blackwell. Staykova-Mileva, E. (2021). Bulgaria. In J. Laine & D. Rauhut (Eds.), Ten statistical briefings on immigration’s social impacts (pp. 25–38). MATILDE deliverable 3.2. https://doi.org/10.5281/zenodo.4726634 Weidinger, T., Kordel, S., & Schomer, L. (2021). Germany. In J. Laine & D. Rauhut (Eds.), Ten statistical briefings on immigration’s social impacts (pp.  61–77). MATILDE deliverable 3.2. https://doi.org/10.5281/zenodo.4726634 Wonnacott, P., & Wonnacott, R. (1986). Economics. McGraw-Hill. World Bank. (2022). Population aged 65 and above (% of total population). Data Code: SP.POP.65UP.TO.ZS.  Retrieved January 3, 2023, from https://data. worldbank.org/indicator/SP.POP.65UP.TO.ZS

CHAPTER 6

Policy Considerations

6.1   Existing Policies To understand the complexity of addressing the economic policy challenges related to the economic impact of a TCN immigration to places beyond the cities, we need to understand the context and premises of the policies and policy making that has taken place until now. The contexts and premises of such policies are however highly politicised, which has left economic arguments unheard. By discussing the impact of the context and premises, we will illuminate the policy gaps towards the policy challenges described in the preceding chapter. The Idea of an A-spatial Economic Structure The New Theory of Economic Growth and the New Economic Geography emerged in the 1980s and focused on the endogenous factors of economic growth. Following the argument offered in these theories, cities played an important role in economic growth (Romer, 1986, 1994; Lucas, 1988; Krugman, 1991a, 1993). During the 1990s and the early 2000s, scholars such as Porter (1990), Castells (1996), Sassen (1991), Florida (2002) and Dicken (2003) argued for the central role cities play in economic growth, but from different perspectives and scientific disciplines. FDI as well as bigger domestic investments usually go to areas located close to the market, with good access to available labour of the correct type and good © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Rauhut et al., The Economics of Immigration Beyond the Cities, https://doi.org/10.1007/978-3-031-30968-7_6

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opportunities for quick returns on investment. Hence, cities and urban agglomerations are favoured, and peripheral and remote areas are disfavoured (Tewdwr-Jones & Morais Mourato, 2005). This is especially so when talking about the post-industrial society, globalisation and the ICT revolution. In the wake of the financial crisis of 2008–2009, it was clear that the economic crisis had hit different types of regions in Europe in an uneven way (Hadjimichalis, 2011; Christophers, 2015; Gruber et al., 2019). In some cases, the austerity measures invoked created ‘places that don’t matter’—places that are usually peripheral, remote and far away from the bigger cities (Rodrigues-Pose, 2018). Economic diversification, the quality of hosted production factors, the density of external linkages and cooperation networks, and the quality of urban infrastructure give greater economic resilience to cities and also to the regions hosting them (Capello et al., 2015). The disadvantages that rural, peripheral and remote places have when it comes to geography and trade reduce their competitiveness and hence their economic growth (Krugman, 1991b). The economic policies in the EU have tended to favour bigger cities and regions with bigger cities (Rauhut & Costa, 2021; Vedrine & Le Gallo, 2021), and in peripheral regions, only the regional capital has been favoured (Nagy & Benedek, 2021). To spread economic growth and reach desired results in these regions, current policy measures need to be reformulated (Medeiros & Rauhut, 2020). The theoretical implications of these spatial aspects were discussed in Sect. 3.4, which lead to the conclusion that we can expect to find different economic impacts of migration for different types of territories. Despite the imposing empirical evidence that economic conditions, economic activities and economic structures differ between different types of territory, the economic potential of peripheral, remote, mountainous and rural regions is recurringly overestimated. Such overestimation is based on the belief that economic activities are ‘a-spatial’. When seen from this perspective, however, these regions constitute an underutilised economic resource (Membretti et  al., 2022), and immigration will help to economically revitalise these regions and mitigate the problems associated with population ageing (Perlik et al., 2019; Rye & O’Reilly, 2020; Przytuła & Sułkowski, 2020). However, these claims have been questioned in other studies, and for example Poot (2008), Gaspar et al. (2005), Coppel et al. (2001) and OECD (2022) emphasise that immigration can both mitigate and aggravate these problems, but not solve them.

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Immigrant Settlement Strategies Generally, policies tackling migration differ, depending on the causative factors that drive the migration decision. Labour immigrants are offered jobs where they are needed, and consequently their settlement is determined by the labour demand in the host country. The settlement of labour immigrants is rather a question for companies than for the government. However, the government is expected to design an adequate and relevant legislation to facilitate the companies with the labour they need. In many countries, refugees are mechanically resettled to peripheral regions to level out population imbalances, adding labour to regions struggling with declining populations (Golebiowska et  al., 2011). The policies of forced dispersal in many European countries which refugees are subject to isolate them from the social networks of previous immigrants that may be critical to job finding and social learning among migrants (Brell et al., 2020). Bansak et al. (2021, pp. 425–426) summarise these challenges in the following way: Most host countries disperse refugees around the country to minimize their impacts. In some cases, refugees are placed in regions that are economically depressed, making it difficult for them to succeed. By placing them in regions with few other immigrants, governments are making it difficult for refugees to access existing migrant networks, which are known to help new immigrants transition to their new environment.

Economically depressed areas usually struggle with unemployment, with few employment opportunities, relatively weak infrastructure and welfare services (Rodrigues-Pose, 2018), and by adding an excess supply of labour without any matching demand or the ‘right’ labour for the existing demand; and as has been discussed in Chap. 3, the situation will not change for the better—neither for the refugees nor for the natives. Notwithstanding, there is a belief in many academic disciplines that refugees will revitalise rural, remote, peripheral and mountainous regions, whether economically depressed or not (Perlik et  al., 2019; Rye & O’Reilly, 2020; Przytuła & Sułkowski, 2020; Membretti et al., 2022). However, policies based on such assumptions usually overlook (and are perhaps ignorant of) some important aspects when resettling refugees to such areas with the hope that they will remain there: (1) There are no or few jobs available for the refugees based on their existing competences and qualifications, which leads to welfare dependency; (2) refugees are seldom

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resource rich and hence have limited money to invest and consume in these areas; (3) refugees and labour migrants are two different kinds of immigrants and cannot be lumped together, nor can they be assumed to become integrated on the labour market in the same way and (4) unintentionally, in many regions beyond the cities, refugees compete with natives for scarce available resources regarding welfare services (e.g., childcare or elderly care) and housing, which sometimes leads to various tensions arising (Gruber et al., 2021; Machold et al., 2021; Krasteva, 2021; Pöllänen et  al., 2021; Kordel & Weidinger, 2021; Gilli & Membretti, 2021; Warhuus Samuelsen & Taivalsaari Røhnebæk, 2021; Lardies-Bosque & del Olmo-Vicén, 2021a, 2021b; Mathisen & Stenbacka, 2021). While this is a very unfortunate development, it is important to remember that this situation is caused by a lack of resources, which is common for all economically weak rural, peripheral, remote and mountainous regions, and not by immigrants per se. Consequently, these findings indicate the importance of not analysing this issue at a national level. Economically, immigrants may mitigate some of the problems with labour shortage, but not the financial problems originating from an ageing population (Coppel et al., 2001; Bodvarsson & Van den Berg, 2013). The situation is far worse in regions beyond the cities than in cities and metropolitan regions. However, even with an unrealistically high labour immigration to such regions, only productivity increases rather than immigration will solve the financial problems created by ageing (Gaspar et al., 2005). The Labour Market Situation for Immigrants There are two key issues to be highlighted related to the labour market situation for immigrants. The first issue addresses to what extent the labour which the immigrant can supply the labour market with is demanded or not, and the second issue addresses the importance of the immigrant being allowed to work. Generally, three major types of immigrants can be identified: economic migrants (e.g., labour migrants and students), forced migrants (e.g., refugees, asylum-seekers and victims of trafficking) and tied movers (e.g., accompanying family members, family reunification and marriage migration). The latter group depends on whether the mover is tied to an economic or forced migrant. While economic migrants are well-­ surveyed in economic migration theories (who migrate voluntarily due to the perceived economic benefits of moving to another country), forced

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migrants do not move due to economic incentives, nor do they move voluntarily (Bodvarsson & Van den Berg, 2013; Bansak et al., 2021). Labour immigrants are demanded and allowed to work. Students may experience some limitations in the number of hours they are allowed to work while studying, but they are also allowed to work, and the likelihood that they will be in demand by the labour market after graduation is relatively high. But when looking at refugees and asylum-seekers, they may offer labour which is not demanded, and they may not be allowed to work while their cases are being processed. Although asylum-seekers and refugees may immigrate without economic motives, this type of immigration generates economic consequences for the recipient country. As refugees have not been economically selected as labour immigrants have, their labour market situation is fundamentally different from that of labour immigrants. Brell et  al. (2020, p.  94) conclude that ‘refugees typically arrive in a host country with less locally applicable human capital, including language and job skills, than economic migrants and consequently are likely to start at significantly lower levels of wages and employability’. The outcome of this is discouraging. Kordel and Weidinger (2018, p. xix) conclude that ‘[a]lthough local stakeholders, such as politicians or entrepreneurs, have high expectations in terms of demographic stabilization and the mitigation of labour shortages, there is little evidence of the integration of refugees in rural employment markets’. According to them, refugees are usually pushed into self-employment for survival. Sometimes the rationale for allocating refugees and refugee reception centres to peripheral and remote areas is that it will generate job opportunities for the locals in economically depressed areas (Mathisen & Stenbacka, 2021). When it comes to labour immigration, the general policy advice is to stimulate highly skilled immigrants to immigrate, while simultaneously raising restrictions against low-skilled immigrants (Kondoh, 2017; Chiswick, 2019; Borjas, 2001). This argument is true, unless this type of labour is demanded in the labour market or the immigrant can quickly acquire the needed qualifications. According to Chiswick (2019), four major clusters exist when it comes to immigration policies in relation to different types of immigrants: (1) One cluster, including the US, prioritises family reunification when it comes to granting visas; (2) countries such as Germany, Japan and Israel form a second cluster putting emphasis on the repatriation of the diaspora when granting visas; (3) a third cluster, including Sweden, prioritises refugee immigration and the fourth cluster (4) containing countries such as

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Canada, Australia and New Zeeland prioritises skill-based immigration when granting visas. Parallel to this, many countries also have special visa systems for high-skilled labour (Chiswick, 2019). Additionally, many European countries still have guest worker systems for international labour. Notably, countries such as France, Germany, the Netherlands and Switzerland apply a guest worker immigrant system (Bodvarsson & Van den Berg, 2013). Spain also has a guest worker system, and there are special arrangements with the Moroccan and Senegalese governments that their citizens can remain in Spain after their work visas have expired, given that they are employed (Bansak et al., 2021). Lately, many countries have started to apply separate visa rules for high-­ skilled labour or STEM-workers (Science, Technology, Engineering and Mathematics). The STEM-workers are highly mobile and there is a huge demand for them in most countries. As employment opportunities for low-skilled labour continuously decrease in manufacturing and agriculture, low-skilled immigrant labour is primarily demanded for low-qualified service sector jobs (Chiswick, 2019). Sweden, which stands out in several ways when it comes to immigration policies, has for many years actually deported high-skilled migrant labour. Although the migrants have permanent jobs in the STEM sector, have bought property, have no criminal records nor welfare dependency, etc., their work visas are not to be prolonged, and they must leave the country once they have expired (Söderqvist, 2021; Teknikföretagen, 2022; Lundstedt, 2022; Eliasson, 2021).1 That Sweden is very generous towards refugees is well known (Chiswick, 2019). During the refugee crisis in 2015, Sweden accepted about 163,000 refugees; while in Europe, only Germany took more refugees in terms of absolute numbers (Laine & Rauhut, 2018; Hagelund, 2020). In relation to their population, first-time asylum applications in 2015 were highest per 1000 inhabitants in Sweden (17.3), followed by Austria (10) and Germany (6) (European Union, 2018). Countries such as Hungary, Poland, Czech Republic and Slovakia did not accept any refugees in 2015, but in 2022, the very same countries received the absolute majority of the refugees stemming from the Russian war on Ukraine (Rauhut et al., 2022). According to Bansak et  al. (2021) and Brell et  al. (2020), there are several reasons why refugees do not perform well in the labour markets in 1  The term in Swedish is ‘kompetensutvisningar’, which means a ‘deportation of competence’. This is an issue which has caught relatively little attention internationally (see, e.g., Lindsay, 2019; Savage, 2019).

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the host countries. The educational attainment levels and human capital of refugees are at lower levels than those of the natives, the refugees have a low language proficiency in the spoken language in the host country, and many of the refugees experience cultural shocks in the host countries. It is however important to remember that refugees never migrated voluntarily, as labour migrants do (Bodvarsson & Van den Berg, 2013). Hence, the outcome of an immigration of refugees can be assumed to differ from an immigration of labour migrants (Hatton, 2013). Nevertheless, many immigration policies target immigrants in general and do not distinguish between different types of immigrants such as refugees, labour migrants, etc. Consequently, the efficiency of the introduction programmes, language training, labour market programmes and anti-discrimination policies is crippled (Bansak et al., 2021). The Role of the European Union The European Union aspires to increase its competencies in many different areas (Smith & Rauhut, 2015). Immigration is one of these areas (Rauhut & Sielker, 2021), but hitherto, the policy success for the EU has been limited, especially as border and immigration issues are related to national security, which means that they belong to the competencies of the Member States (Hantrais, 2007). However, this does not mean that the EU has not tried to expand its domains of competence. While the Treaty of Rome (European Union, 1957) guaranteed the free mobility of labour within the Member States, and this is one of the four key pillars of the European Union, these rules do not apply to extra­EU immigration. In the mid-1980s, the Member States started to apply more restrictive policies for non-EU immigration; hence, the term ‘Fortress Europe’ came to be invented (Hantrais, 2007). The US ‘Green Card’ system was copied by the EU in 2009 when introducing the ‘Blue Card’. The aim was to attract non-EU citizens to immigrate, and it offered a streamlined procedure for work permit applications. The basic requirements were simple: non-EU citizenship, completed higher education degree and a job contract. These rules are binding for all EU Member States, but Denmark and Ireland have chosen not to participate in the ‘Blue Card’ programme (Bansak et al., 2021). This raises the question of how binding the EU rules are. In fact, the EU rules rest on a decision model called the Open Method of Coordination (OMC), which is a very

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soft decision making and implementation model.2 After all, given that the EU has no competence in this area, the ‘Blue Card’ programme cannot overrule the national competence of the Member States. When it comes to refugees and asylum-seekers, the policy competence lays with the Member States. Again, the EU has tried to play a role in areas outside its competencies, but the Dublin I, II and III conventions are exceptions. The Dublin Conventions aimed at bringing some structure to the processes of asylum applications across the Member States. Similar rules should be applied in all Member States and the asylum-seeker should only be allowed to apply for asylum in one country, which must be the first EU country the asylum-seeker arrives at (European Union, 1997; Council of the European Union, 2003; The European Parliament and the Council of the European Union, 2013). However, the Dublin Conventions never managed to create a fair distribution of asylum-seekers across the Member States, and the countries with extra-EU borders have continued to receive most of the asylum-seekers (Guercio, 2019; Rauhut et  al., 2022). The explanation as to why no such redistribution mechanism has been achieved can be traced back to the OMC principle. Moreover, although the Dublin III Convention is still in force, its implementation is no longer strict. The convention collapsed during the refugee crisis in 2015, and during Russia’s war on Ukraine little effort has been taken to restore it (Rauhut et al., 2022). The European Union has made several attempts to set up rules for immigration (e.g., CEC, 2000, 2015, 2020a) and for the integration of immigrants (e.g., CEC, 2004, 2005, 2020b). As immigration issues are in the competencies of the Member States (Hantrais, 2007) and so are social policy and welfare issues (Anderson, 2015; Szyszczak, 2013) to which immigrant integration formally comes under (Rauhut & Sielker, 2021), the OMC rules apply. When reviewing the immigration policies for the eight selected EU countries, the national governments are sovereign regarding immigration rules to these countries (Dax et al., 2021; Hansson, 2021; Laine et  al., 2021; Lardies-Bosque & del Olmo-Vicén, 2021c; Membretti et  al., 2021; Staikova-Mileva, 2021; Taivalsaari-Røhnebæk et al., 2021; Weidinger et al., 2021). 2  The OMC is based on the voluntary cooperation of its Member States and rests on soft law mechanisms such as guidelines and indicators, benchmarking, and sharing best practice. This means that there are no official sanctions for laggards or ‘cherry-picking’. Rather, the effectiveness of OMC relies on a form of peer pressure, and naming and shaming, as no Member State wants to be seen as the worst in each policy area (Pochet, 2005).

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The magnitude of the refugee flows to Europe over the last decades raises the question of to what extent every single EU Member State has the capacity and will to deal with the challenges of refugees (Straubhaar, 2015; Altemeyer-Bartscher et  al., 2016). An increased cooperation between the EU Member States is definitely needed (Lücke, 2018), but most likely it will be insufficient to address the challenges posed by refugee reception. It therefore appears reasonable to raise the question of to what extent a redistribution of competences between the EU and the Member States could mitigate, alleviate or even solve the problems encountered. But, this is a delicate policy issue.

6.2  The Needed Policy Design A troublesome policy aspect is that immigrants are considered a homogeneous group, while in fact they are not. Labour immigrants have a job waiting for them upon arrival, which means that they generate an economic surplus from day 1, whereas refugees need to be provided for until they can provide for themselves, which can take quite some time. Moreover, the policy actors involved in the economic aspects of immigration and immigrant integration are not only heterogeneous, but they also operate at different levels in society, reflecting a multi-dimensional governance system (Fuertes & McQuaid, 2013). These types of system are well-known for leading to ‘de-coupling’ problems and reduced efficiency (e.g., Scholten, 2016; Gruber & Rauhut, 2023), and ‘one-size-fits-all’ policies do not reduce this problem. The Economic Heterogeneity of Immigrants From an economic perspective, immigration policy should generally be designed to avoid a situation where immigrants burden the destination country economically and receive net transfers from the natives. Labour immigrants are demanded on the labour market, and they are allowed to work. However, for refugees and asylum-seekers the situation is different as the labour they offer to the labour market is not always in demand and they are seldom allowed to work during the review process of their cases. Furthermore, they are not granted a permission to stay because of their human capital, labour or potential economic profits for the host country, but for other reasons. For asylum-seekers such reasons include, for example, humanitarian reasons and being granted refuge from different forms

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of prosecution (political, ethnical, religious, sexual, etc.). Quota refugees have already been classified as refugees by the United Nations High Commissioner for Refugees (UNHCR) and hence need resettlement in a third country. Every country can decide on how many quota refugees they would like to resettle, and by accepting them, the host country takes responsibility for the resettling process, including housing, education, work, etc. (UNHCR, 2018). An asylum-seeker applies for asylum at the border of a country and has his/her case reviewed in that country. If the asylum-seeker is granted refugee status, the host country has the responsibility to resettle the refugee in the country (Garcés-Mascareñas, 2015; Brell et al., 2020). Although the costs for resettlement will be high, they are considered to be politically acceptable (Becker, 2022). According to Chiswick (2019), it is important that immigrants with the greatest potential to bring added value to the host country are selected for immigration. This does not necessarily mean that they are labour immigrants, but to base immigration on low-skilled and tied movers is costly for the host country. In most EU countries, immigration is dominated by low-skilled immigration, and this is especially so in rural, peripheral, remote and mountainous areas (Laine & Rauhut, 2021; Kordel, Laine, et  al., 2020a; Kordel, Lund, & Dahl, 2020b; Kordel, Amcoff, et  al., 2020c; Spenger & Krasteva, 2020; Spenger, Gruber, & Machold, 2020a; Spenger, Weidinger, et  al., 2020b; Weidinger, Bergamasco, et  al., 2020a; Weidinger, Lardiés Bosque, & del Olmo Vicén, 2020b). However, it is worth remembering that this does not include intra-EU migration as these migrants are not considered as immigrants according to the Maastricht Treaty. As discussed in Chap. 3, immigrants who are not demanded on the labour market and who are not allowed to work can be expected to generate net transfers from natives until they can support themselves economically. As Gustafsson et al. (2017) show, it takes on average 17 years for a refugee from a non-OECD country to obtain one year of consecutive employment in Sweden. Worth noting is that there are huge variations depending on age, human capital and the country of origin in how long it takes to achieve 12 months of consecutive employment. Notwithstanding this, it means that a country receiving refugees and asylum-seekers must be willing to accept the costs it takes before the refugees and asylum-­ seekers can support themselves. If the costs are considered as too high, the policies regulating the different groups of immigrants should be adjusted. However, these costs are not economically but politically determined.

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As long as the costs are politically acceptable, it does not matter if they are economically questionable, as we are talking about humanitarian aid where the economic aspect of refugee immigration is subordinated to the political aspect. The Policy Actors When discussing immigration policies and how they are designed, it is important to discuss which actors will benefit from the political economy of immigration. Three key actors can be identified: (1) the government, which is indirectly appointed by the electorate—that is the citizens of the country; (2) companies and (3) individuals. The immigration policies are designed to favour—or at least not disfavour—most of these actors economically. First, when it comes to labour migration, companies and employers benefit from generous labour immigration rules, and labour shortages and bottlenecks in production caused by labour shortages can be mitigated (Lundh & Ohlsson, 1999; Schön, 2000). The destination country and the taxpayers also benefit from labour immigration. In the short run, the net transfers from labour immigrants to natives will be positive, which enriches the natives (Simon, 1998). However, a large-scale labour immigration of low-skilled immigrants for low-productive jobs will slow down or even obstruct structural change in the economy, which is negative for the economy at a macro level (Lundh & Ohlsson, 1994). Furthermore, low-skilled immigrants working in low-paid and labour-intensive jobs are more dependent on welfare systems than natives (Borjas, 2001; Stark, 1991; Bodvarsson & Van den Berg, 2013), which has an additional economic impact. Second, the government should ensure that the macroeconomic impact of immigration is positive, and if not, it should act in the interests of the taxpayers. Immigration laws should restrict immigration for groups for which large net transfers from natives are common (Kondoh, 2017; Borjas, 2019; Chiswick, 2019; Ruist, 2015). However, the long-term perspective should also be considered in any macroeconomic assessment of the costs/ benefits of immigration. Although companies and employers cry out for labour to mitigate a labour shortage, labour immigration should not be allowed to become a net burden from a macroeconomic perspective by slowing down or obstructing the structural change of the economy

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(Lundh & Ohlsson, 1994), and the government should prevent such immigration. Third, the individual is also an important actor when talking about the economic consequences of immigration, regardless of whether the individual is an immigrant or a native. Immigrants can benefit from migration in terms of things like higher incomes, better material standards, better educational opportunities and a higher quality of life (Bodvarsson & Van den Berg, 2013). But forced migrants and refugees lose out economically, sometimes for generations (Becker & Ferrara, 2019). In the case where immigrants produce net transfers to the natives, immigration is good for the native population, but if there is a net transfer from natives to immigrants, the natives must finance these transfers by way of higher taxes (Borjas, 1994; Mayr, 2012; Römer, 2022).3 Theoretically, we argue the costs could also be paid through reduced public services or a reduced quality of public services, but while in non-metropolitan regions this would entail troublesome cut-backs, in urban areas and bigger cities with functioning markets for services, this would probably not be seen as a problem. The marketisation of welfare services has hit the places beyond the cities hard (Lobao et al., 2018) and so has the job destruction that follows in the wake of de-industrialisation (Essletzbichler et  al., 2018). Since many non-metropolitan regions (and especially peripheral, remote and mountainous regions) struggle with market failures and missing markets, natives and immigrants compete for the same (limited) resources. However, the immigration policy should be designed to avoid such situations, and externalities such as conflicts, discrimination and social tensions will cost money for the taxpayers and put even more pressure on regions that are already economically weak. To avoid net transfers from natives to immigrants, the immigration policy should require that immigrants should be able to prove that their income is sufficient to feed themselves and any dependants. However, the costs caused by persons who have been granted refugee status must be accepted.

3  The cited authors discuss different aspects of this, but it must be kept in mind that their analyses do not distinguish between labour migrants and refugees, nor is there any spatial differentiation on the effects in their analyses.

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Policy Challenges to Address The findings in this study point at some urgent policy challenges that need to be addressed. The share of TCN is lower in non-metropolitan regions that are at the same time affected by an ageing of the population. Thus, increased immigration of TCN may reduce the speed of ageing in such areas. Nonetheless, this does not necessarily result in the desired economic effects of immigration, as the educational level is on average significantly lower for TCN compared with other groups. Given the fact that TCN consist of two main groups of demanded immigrants (based on labour market needs) and not demanded immigrants, this bimodal distribution has to be considered. When high-skilled migration into areas beyond the cities is needed, an existing demand in the labour market will be met by this kind of immigration. However, lower educational attainment levels lead to problems in getting a job in the analysed knowledge-intensive economies, for immigrants as well as for natives. The immigration of low-­ educated immigrants to places beyond the cities often generates an excess supply of low-skilled labour with no matching demand. Only in cases with an existing demand for labour with a relatively low qualification profile (e.g., in agriculture and fishing, and the manual manufacturing or service sectors) can the desired positive effects be expected. This would imply a distribution or redistribution of migrants according to their profiles and local demand, but not necessarily according to the existing politically determined distribution schemes or the migrants’ own preferences. A second finding challenges the hope for immigrants to revitalise places beyond the cities economically by starting companies providing goods and services to the local market. Instead, we find a negative correlation between the share of TCN and business start-ups. While some positive effects can be expected due to their consumption expenditure, the relative worse labour market situation present in such areas may restrict this effect from developing. A third finding points at the fact that immigration can only partly be used as a tool for population growth or rather as a mitigation of depopulation and ageing. The places beyond the cities are quite unattractive for immigrants from outside the EU, which can be partly explained by, for example, missing networks, the weak economic structure, poor employment possibilities and few services. However, we also find a variation between countries.

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Other challenges involve factors such as language proficiency and a recognition of qualifications and experiences. When solved, the process of becoming integrated in the labour market in the community in which the immigrant resides will definitely be smoothened. However, integration is a two-way process in which both natives and immigrants need to be active. Labour immigrants with fixed-term contracts may not find it worth the time and effort needed to learn the language, especially if they just plan to stay for two or three years. Moreover, many asylum-seekers may be traumatised by their escape from their native country, and many plan to return ‘home’ as soon as possible. It is understandable that learning a new language is not going to be at the top of their personal agenda (Rauhut & Laine, 2023). However, at a certain point, after residing for many years in the new country, both groups realise that they need to learn the language. Without a language proficiency it will not only be more difficult to get to know the new country but also to get a job. The host community also needs to reflect and decide on what the minimum language requirement needed for the new labour force is. On top of this, however, there are unfortunately both immigrants and natives who are uninterested in becoming involved in the integration process (Rauhut, 2020; Rauhut & Laine, 2023), and it is a delicate political question as to how to deal with them. Immigrants (and especially asylum-seekers) have had difficulties to establish themselves on the labour market in regions beyond the cities. No doubt they contribute to the local communities in which they live in different ways, but their impact in rural, remote and peripheral regions is quite modest (Rauhut et  al., 2023; Kordel & Weidinger, 2018). Conventionally, immigrants are considered to become integrated when they obtain a job (e.g., Ager & Strang, 2008). However, the labour market works differently, and as about 75% of all vacancies are filled through personal networks, obtaining a job is rather an indication of being successfully integrated as the immigrant has successfully managed to build networks into the labour market (Macuchova & Rauhut, 2023). Moreover, many of the immigrants have a low educational level, which means that they must improve their human capital to become employable in the modern knowledge-intensive economies most EU countries represent. Without investments in human capital, immigrant labour has difficulties in becoming integrated in the labour market. Here, several urgent issues arise, such as Who will pay for the educational investments? What labour demand

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should the educational investments meet? Where will the education take place geographically? These questions are politically delicate. The impact of informal institutions is strong on the integration process, both on immigrants as well as natives. Norms, preferences and values impact the social interaction between natives and immigrants if they want to interact with each other and also to what extent they interact. The unwillingness of natives to interact with the newcomers is often referred to as discrimination and racism, and represents a challenge for getting an integration process started—as the proverb states, ‘it takes two to tango.’ However, this is also true for some (but far from all) immigrants, and some immigrants have little interest in learning a new language and/or entering the labour market. Informal institutions from the immigrants’ native countries related to, for example, gender roles, female work outside the household, education for women or men talking orders from female supervisors/managers present a negative inertia to their integration into the host community. Changing informal institutions such as norms, values and preferences for both natives and immigrants constitutes a delicate policy challenge. So far, we have expressed concerns over the willingness of all immigrants in regard to what extent they would participate in education, vocational training and apprenticeships in order to qualify for jobs in the community in which they live. We have also expressed concerns regarding the natives and how new members in the community are welcomed. In many regions beyond the cities, newcomers are viewed with suspicion, and if there is no change in the culture towards (im)migrants, it will be difficult to make them stay (see Rauhut, 2020). Seen from an economic perspective, racism and discrimination by natives towards immigrants are costly issues for the host community. The integration of immigrants is an informal institution, and as such, unfortunately, it may take quite some time to change (Rauhut, 2020; Penninx, 2003). When addressing these policy challenges, it is of utmost importance to remember that the immigrants do not constitute a homogeneous group of people, that economic development displays significant spatial variation and that different policy actors have different and sometimes conflicting interests.

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6.3   Potential Ways Forward A New Classification for Migrants Drawing from the delineations above, the question of whether and how immigrants can be integrated in the labour market seems to be decisive for answering the question about their economic impact on the regional level. Therefore, to focus the characteristics of the respective individuals that determine whether it can become economically active presents a constructive approach. However, immigrants appear to be analysed based on their cause of stay (labour immigrants, students, family unification/tied movers, students, refugees/asylum-seekers) in the literature (Ruist, 2022; Constant & Zimmermann, 2013; Kondoh, 2017; Chiswick, 2019; Borjas, 2019; Bansak et al., 2021; Bodvarsson & Van den Berg, 2013; see also Garcés-Mascareñas & Penninx, 2016; Scholten et al., 2015). Alas, such classification does not contain any information on what potential the immigrants hold to become integrated in the host community, nor to what extent they will be economically beneficial for the host community. Hence, we would like to propose a somewhat different analytical scheme, focussing on two analytical dimensions: (1) to what extent the immigrants are allowed to work and 2) to what extent the labour these immigrants offer to the labour market is demanded. The first aspect of being allowed to work usually depends on the legal status of the immigrant—labour migrants come with a permit to work (and usually already a fixed job promise or contract), while asylum-seekers usually do not have a work permit. The same applies to family members who usually do not have a work permit from the very beginning, but in many cases can acquire it later. Illegal migrants by nature do not have permission to work, but also may be able to acquire it in the long run, for example, if their legal status changes. The second aspect relating to the demand for immigrant labour depends on the qualifications and abilities of the individual migrant and also the structure of the regional labour market. It is specifically these factors that determine if a workers’ qualification and skills are ‘demanded’ or not. While labour migrants will usually display the qualifications and abilities that are sought after in the labour market, refugees and family members may or may not have them, with the distribution being somewhat random. The same applies to illegal migrants where some of them may have qualifications and skills needed on the labour market, and some of them

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Table 6.1  Alternative analytical dimensions

Demanded Not demanded

Allowed to work

Not allowed to work

Entry in the labour market possible Skill/qualification mismatch

Legal constraints, thus no entry in the labour market No entry in the labour market

Own elaboration

may not have them initially, but may be able to acquire them in the medium- or long-term. Based on these considerations, the following analytical matrix (Table 6.1) can be derived that may form a basis for further research on how to design immigration and integration policies for different migrant groups. The questions surrounding whether economic activity is possible, the demand for specific qualifications or education in the labour market, as well as the formal status of individuals are not necessarily based on stable traits or features. Several classifications are only valid for the short-term, while in the medium- or long-term, the characteristics of the migrant may change. This can occur due to the natural passage of time (e.g., when it comes to the age of the individual), individual adjustments or activities (e.g., the acquisition of specific qualifications or an increased education level/recognition of educational levels), and also legal changes (e.g., regarding work permission) and specific policies. Thus, the qualifications and skills of the migrants as well as the regional economic structure or legal frameworks are not seen as being in a stable state, but dynamic, making traditional classifications of migrants versus refugees somewhat blurred or useless. Customising demand-based education for professions struggling with a labour shortage is a policy option to be considered that could facilitate labour market entry for immigrants. To graduate from such education processes would be more likely to lead to an employment. Offering apprenticeships for more practically oriented professions is already used in many countries, both for natives and immigrants. But the challenge for both demand-based education and apprenticeships is to make the target group participate. Given that in many countries, for example, women are not supposed to perform work outside the household, this particular

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group of immigrants may not want to take part in this and poses a delicate political question of how to address this challenge. As has been noted, ‘migrants have increasingly diversified profiles, and their status can change in the course of a journey’ (UNHCR, 2022, p. 14). The same applies to the requirements in the labour market that are becoming more and more dynamic. Hence, the characteristics of the migrant as well as those of the labour market may change with the lifecycle of the migrant, but also with the economic development level of a regional entity, industrial change or innovations in production and service delivery (Rauhut, 2007). Thus, the time frame must be taken into consideration when analysing the effects of migration in the labour market and designing policies to tackle labour market integration. Scrutinising aspects related to legislators’ terms and the policy cycle involved, this implies dynamics, on the one hand, and a long-term orientation, on the other. A New System of Distribution of Refugees The distribution or redistribution of refugees in most countries does not follow an approach that takes into consideration the regional or migrant’s characteristics, but is based on a more political logic. In many countries, refugees are recorded and initially distributed on or close to their first place of arrival and then redistributed in line with either politico-­ administrative agreements amongst the regional entities or a logic of free space in the reception quarters. Thus, any alignment of policies according to the logic of the labour market or other socio-economic features of regions may necessarily imply a change of existing governance schemes, particularly for asylum-seekers and refugees. Overall, a multi-level system that includes the European level as well as the national and the regional levels would be necessary to ensure that migrants (including those that are not yet allowed to work) settle in regions where they are or will be in demand once their status has changed. A system like this would necessarily imply that the regional (or even municipal) level is directly involved in the (re)distribution of migrants from the very beginning (Schomaker & Bauer, 2019). This implies not only a change of many national refugee governance systems but also a coordination with the European system of migrant redistribution, as the national level (if involved in the EU redistribution system) would have to recognise the regional demand for labour and people, as well as the capacity to absorb newcomers in the local and regional labour markets.

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In detail, a matching mechanism such as that used in university applications could be used, in accordance with European data protection regulations. Organisationally, such a solution seems conceivable, and criteria for a central matching of refugees could be queried relatively easily and managed via a central database, both at the individual and municipality levels. In addition to the criteria specified by the public side, applicants would have to provide information about their existing skills (such as language skills or job opportunities suitable for training) and could highlight additional criteria regarding issues such as the socio-cultural characteristics of the host area, family ties and cultural networks (Schomaker & Bauer, 2019). In this context, it is important that individuals receive accurate and comparable information about the communities they may enter (Jones & Teytelboym, 2017). Some first initiatives in the delineated direction have currently emerged, such as a matching mechanism for high-skilled refugee researchers (CEC, 2023b) or a support tool created for institutions offering assistance to TCN (CEC, 2023a). Nonetheless, these initiatives either tackle specific groups and/or are just voluntary tools that connect individuals with institutions or potential employers, but do not include the public sector or imply any legal aspects of settlement. The Chicken or the Egg: Which Came First? Being employed and in the labour market is a key indication that is used when analysing integration. If an immigrant holds employment, they will have income, housing and a social life—that is they will be integrated into the community in which they live (e.g., Ager & Strang, 2008; Hansson et al., 2023). The causality between employment and integration is clear: employment leads to integration. But as already expressed, this causality can be questioned. In most modern knowledge-intensive economies, approximately three out of four vacancies are filled through networks, which means that an immigrant without any local networks is disadvantaged on the labour market. Macuchova and Rauhut (2023) argue that these networks are the key to a successful integration, but to become a part of these networks, the immigrant must be integrated into the society in which she/he lives. Stenbacka (2023) identifies the importance of being a part of local networks for job-seeking and also their role in the integration process. Hence, Macuchova and Rauhut (2023) conclude that the causality is in fact the other way around: obtaining employment is a

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function of being integrated, and hence having networks helps in the job search. Inevitably, this leads to a situation that is analogous to the question of which came first, the chicken or the egg? Is employment a result of successful integration, or is successful integration a result of having employment? By placing refugees in peripheral, remote and mountainous areas, they become isolated from the social networks of fellow countrymen that are seen as vital for job searching and social learning (Brell et al., 2020). Many of the peripheral, remote and mountainous areas are far from economically prosperous and expanding regions, which makes it even more difficult for refugees to find jobs (Bansak et  al., 2021). Thus, it is little surprise that refugees seldom become integrated on the labour markets in those areas (Kordel & Weidinger, 2018), as they lack the needed social networks to get a job. In the cases where refugees have social networks (i.e., they are integrated into the community in which they live), they have far better chances of finding a job (Stenbacka, 2023). We must bear in mind that labour immigrants commonly have a job waiting for them on arrival, and as having a job helps to get a job, labour immigrants have an advantage over refugees in the search for a (new) job (Macuchova & Rauhut, 2023). As such a causality may be the opposite of what is conventionally believed, future policies to facilitate labour market entry for immigrants should perhaps change their focus. In relation to this, one also needs to reflect on how and what role the recognition of qualifications and experience should play in the efforts to get more immigrants integrated in the labour market. For some professions (e.g., medical doctors, nurses), a labour shortage in regions beyond the cities may be mitigated if the recognition process is sped up. However, for some other professions this will be of little help as the competence is often country- or place-specific (e.g., lawyers).

6.4  Concluding Remarks Immigrants are heterogeneous and cannot be considered to generate the same sorts of economic consequences. Moreover, where immigrants geographically settle down will have an impact on what economic consequences they will generate. Consequently, when designing policies, the focus should not be solely on the theoretical potential that, for example, refugees may revitalise the economy in peripheral, remote and

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mountainous regions and counteract demographic ageing (Greve-Harbo et al., 2017; OECD, 2022). Even when it comes to the more economically beneficial labour migrants, the actual economic gains do not meet the theorised expectations (Borjas, 2019). Attention must also be given to the actual outcomes of the resettlement of refugees to peripheral, remote and rural regions. Refugees seldom revitalise these regions economically and do not become integrated in the local labour markets or socially (Brell et al., 2020; Bansak et al., 2021; Kordel & Weidinger, 2018). To become competitive in the knowledge-intensive modern economies, significant investments in the human capital of immigrants are needed, but it is unclear who will finance this. The common view is that employment will lead to integration, but as three out of four vacancies are filled through personal networks, the causality can be questioned. It is rather seen that you need to have a job to get a job, which implies that integration must take place prior to employment. Education, language proficiency and social networks are key pillars for immigrants on the labour market, and without them, finding a job is difficult. In this case we can expect a negative impact from immigration. However, economic consequences can seldom be described as entirely positive or negative, but rather as different shades of grey. When immigrants are quickly integrated into the labour market and have a consumption level relatively similar to the natives, we can expect situations where immigrants stimulate positive economic consequences in the economy. Most likely, we can expect both things happen simultaneously. The European Union would like to expand its influence on immigrant integration as well as other social policy areas, but the competencies in these areas lie with the single Member States. The inability to reach an agreement on a union-wide refugee re-allocation mechanism illuminates the fundamental disagreements on how many refugees should be allowed in and how to distribute the costs. Without competencies being moved from the Member States to the EU, the EU will continue to have a minor role in this policy area. But even with this said, we must also ask ourselves if we actually believe that a single EU Member State has the capability of handling the economic costs for hosting refugees. As an example, the 2015 refugee crisis stirred up a lot of economic, social and political turbulence across the EU, and yet, it only involved about 1 million refugees. With 10 million Ukrainians seeking refuge from Russia’s war on Ukraine, we can expect more economic, social and political turbulence ahead.

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CHAPTER 7

The Multi-faceted Implications of Immigration: Reflections and Conclusions

7.1   What Are the Main Findings? The analysis in Chap. 5 uncovers findings suggesting that regions beyond the cities are going to be particularly demanding labour in the upcoming years and decades. Thus, the migration of individuals who are in demand on the labour market and allowed to take on the respective jobs may mitigate some of the existing challenges. Nonetheless, the economic impacts of immigration for regions beyond the cities are different than the impacts on cities. Several notable empirical findings can be identified: (a) The share of immigrants with a low educational level in non-­ metropolitan regions is generally far higher than the share of citizens in the reporting country with a low educational level. Moreover, the share of immigrants with tertiary education in non-­ metropolitan regions is generally lower than the corresponding share of the population in the reporting country in these regions. This is also true for metropolitan regions, but the educational level in non-metropolitan regions is generally lower overall. (b) The migration or allocation of unskilled immigrants to each type of territory may generate an excess supply of labour with no matching demand on the labour market. When the share of immigrants with primary education increases in non-metropolitan regions, so does

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the unemployment rate, and when the share of immigrants with primary education increases in non-metropolitan regions, the employment rate decreases. The same impact cannot be observed in metropolitan regions for immigrants with primary education. Moreover, for immigrants with tertiary education, no significant correlation with unemployment or employment can be observed in metropolitan or non-metropolitan regions. (c) The migration or allocation of unskilled immigrants to metropolitan regions displays a negative and statistically significant correlation with economic growth, while no significant correlation can be observed in non-metropolitan regions. Immigrants with tertiary education display a positive and statistically significant correlation with economic growth in metropolitan regions, but not in non-­ metropolitan regions. (d) The share of immigrants in the regional population displays a weak negative correlation with company start-ups in non-metropolitan regions, but for metropolitan regions the findings are inconclusive. If the immigrant population is analysed by educational level, the correlations are statistically insignificant. However, previous research has noted that immigrant entrepreneurship outside the major cities is modest and offers no solution to the unemployment immigrants struggle with (Kordel & Weidinger, 2018; Rauhut & Rauhut Kompaniets, 2018). (e) Positive effects through consumption can be expected from any kind of migration. Nonetheless, as consumption is driven by income which is closely correlated to education and being an active member of the workforce, this effect will be lower for migrants with low qualification profiles and unemployed migrants. Thus, the economic net effects for specific areas depend on the profile of the immigrants, as well as on the question of who finances integration and social services—the regional entity itself or other levels. (f) Theoretically, this volume offers a way forward when analysing immigrant integration. Refugees and asylum-seekers are theoretically difficult to analyse using conventional economic migration theories, and the causes of migration are not based on economic incentives, but rather on force or coercion. However, this does not suggest that economics is of no interest or even incapable of analysing the labour market performance of immigrants. Labour market economics, and especially the parts related to an excess supply of a

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certain labour in the labour market, offer significant theoretical insights on how such labour will perform on the labour market and what the challenges are. Theoretically, undemanded labour will experience a low employment rate and a high unemployment rate. This will in turn lead to lower incomes and inactivity. Moreover, an out-migration to bigger cities in the hope to get a job is to be expected. There is empirical evidence to support this in regions beyond the cities. Additionally, according to theory, a marginal position on the labour market should lead to a higher degree of entrepreneurship and self-employment, but for immigrants the empirical evidence indicates the opposite. (g) If the migrants are demanded on the labour market, then positive economic effects for the regions beyond the cities are to be expected. While low-skilled migration might hinder the out-­ migration of companies in the short run, it might also hinder structural change in the long-term. High-skilled migration that is demanded on the labour market might also lead to economic growth, as structural change to a knowledge-intensive society might be accelerated.

7.2   Policy Options Based on the policy discussion in Chap. 6, several potential routes for policies to address the findings outlined above can be identified. While some of them appear to be conventional (but need to be repeated), others are new, and some are even bold. Among the conventional policy options to address poor language proficiency and low educational attainment levels are language training, vocational training, demand-based education and apprenticeships. In many cases, these work relatively well, and many immigrants are helped by these actions to enter the labour market. These policies are important and need to continue. However, although most immigrants participate in these actions, there are also those who do not and who have little interest in becoming integrated. To address these issues, unconventional policies may be needed, and we return to this below. The territorial distribution of immigrants is not optimal from an economic perspective. Especially, low-educated immigrants are allocated to or allowed to settle in regions beyond the cities, without consideration of whether their labour is demanded. While labour immigrants settle in areas where their labour is demanded, this is not the case with refugees and

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asylum-seekers, no matter what their qualification level (and therewith their potential or future contribution in the labour market). In many cases, they are allocated to non-metropolitan regions, for example, in the belief that there is more housing available in these regions than in the cities, or as national allocation schemes foresee such a distribution. However, this is not always the case. Importantly, to let refugees and asylum-seekers settle down where they want to will put an enormous pressure on the housing market in the metropolitan regions, so this does not represent a sustainable policy option. To enhance the economic sustainability in the territorial distribution of immigrants, more innovative policy approaches can be applied. At the European level, ‘matching mechanisms’ are discussed. However, to make such a policy intervention applicable at a national and/or regional level, not only legal changes and a strict data protection mechanism are needed. This also implies the willingness of Member States and the wide range of regional public entities to cooperate, and not least the willingness and ability of the respective migrants themselves. Thus, while it may not be a short-term solution, it may provide a sustainable approach. The reception of refugees and asylum-seekers is social policy issues within the competence of the EU Member States, and the failure to agree on an EU-wide redistribution system can be explained by the prevailing national interests present among the Member States. The magnitude of the recent refugee flows in 2015 and the current refugee flow in the wake of Russia’s war in Ukraine illuminate the challenges that single Member States encounter with regard to refugee reception and immigrant integration. No doubt a controversial issue in our proposition, but we think it would be to the benefit for all parties concerned if at least some parts of the division of competence between the EU Commission and the single Member States were to be re-negotiated. It is simply unrealistic for a single or a few Member States to shoulder the economic burden and social policy challenges of the reception of refugees and asylum-seekers. Also, the discrimination and racism of natives, as well as a potential disinterest of migrants in learning a new language and/or entering the labour market, are relevant challenges. These challenges are delicate, palpable and need to be overcome. If you want to make people change their behaviour or adopt a desired behaviour, you need to press the right buttons. The most common tools for doing this involve the use of ‘the stick or carrot’—that is incentives and disincentives. In many countries, immigrants lose their economic support, allowances, etc., if they do not

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participate in various activities. This is, of course, one way to go policy wise, and if immigrants do not, for example, pick up the new language in a certain period of time, are not active on the labour market or do not settle down where there are available jobs, they can be penalised. However, we are not convinced that this is the most efficient approach. On the contrary, it may even backlash on the host community, as those immigrants who had little interest in integrating to start with will definitely turn their back on the host country and turn introvertly towards their own ethnic group if they are penalised. Moreover, there are few policy tools to make natives change their attitudes and behaviour if we penalise undesired behaviour. Instead, we would like to propose one of the most efficient tools to make people change behaviour: that of economic incentives. If the immigrant receives an economic award for passing a language test, their interest in learning the new language will increase. When the immigrant receives an economic award for passing a civics test, their interest in learning about the new country and passing the test will increase. Moreover, if the immigrant receives an economic award for moving to regions beyond the cities, then many may do so. The same stimulus can be used for immigrants who complement their foreign education to get a degree in the new country. Furthermore, extra awards could be offered to women from countries where women seldom have more than a primary education if they pass the educational system in the new country and start working outside the household. How high these economic awards should be is open to discussion, but they need to be high enough to produce an offer that no household can reasonably refuse. Initially, it may be costly, but over time we expect this to be beneficial for the host community. Moreover, the source of such funding can be discussed. With a view on the potential role of the EU, some of the funding such as particular payments for taking up residency in remote or rural regions may originate from, for example, Cohesion funds (Schomaker & Bauer, 2019). It must be emphasised that this system of economic stimulus to change behaviour can also be targeted at natives to make them, for example, move to regions beyond the cities, to finalise their education or to make women leave domestic work. Moreover, natives who are actively involved in integration activities could benefit from this economically. Last but not least, to continue to use a categorisation based on the causes of stay may not be the best suitable approach. As we noted earlier, to what extent the immigrant is allowed to work in the new country or not and if his/her labour is demanded or not are interesting aspects to

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consider. Labour immigrants who lose their jobs do not always find it easy to find a new job, and while their labour was in demand by one employer, but it may be of little interest for other employers. Moreover, refugees and asylum-seekers are usually not allowed to work while their case is being reviewed, but their labour may still be in demand. The efficiency of integration policies may be increased if the policy design focuses more on the immigrant being allowed to work or the demand of the labour market, rather than on a blunt categorisation based on causes of stay.

7.3  Looking Onwards and Outwards Given the relevance of the topic, it has to be said that existing data restrictions on regional and municipal levels have so far created barriers for comparative research, not only for the European Union but also beyond. To understand the possible economic potential and challenges that the migration of low-skilled migrants particularly implies, the generation of data on this topic is a necessary precondition. In this context, the use and identification of alternative indicators may be useful. For example, information on the wage levels of different population groups (natives and migrants, distinguished by being workers and/or capital owners) would be needed in order to analyse effects of migration on income in detail. In this volume we have analysed cities and regions beyond the cities at NUTS 2 level throughout the European Union. As delineated above, the relative scarcity of studies on the economic effects of migration at the regional level deserves further attention. Depending on the country and what NUTS level is most relevant, similar analyses could be undertaken at NUTS 3 level or even on LAU1 or LAU2 levels. Especially, some countries have micro data which could be used to explore the sub-national and territorial aspects of the economic impact of immigration. The necessary precondition for such research is the existence and accessibility of data on different regional levels. This data must not only comprise of traditional indicators such as GDP or educational attainment but may also consist of more detailed information about, for example, the sectors of employment of different groups such as natives, TCN and EU citizens. Moreover, to capture the dynamic nature of migration and migrants, and including changes of legal status or educational level, new kinds of indicators would be necessary to collect that coherently cover longer time periods.

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As delineated in Chap. 6, the traditional understanding of migration may be another obstacle for research. The classification of labour migration, on the one hand, and refugees or asylum-seekers, on the other, may no longer be suitable for several reasons. First, there is not always a clearcut reason why people migrate, and in many cases, economic motives such as striving for better jobs are associated with push factors such as political unrest or personal security constraints. Second, it is less the actual status quo, but more the potential of individuals that matters for assessments of their demand in the labour market. This is particularly true when viewed against the backdrop of the dynamics related to the individual (e.g., educational level or resident status), but also the changing regional patterns involved. Hence, a classification according to the dimensions of demand in the labour market and work allowance mentioned above may help to overcome this problem. When placing the findings from this study in a broader context, it is possible to identify several areas where scientific theories, empirics and politics march at different paces and in different directions. One such area relates to what causes economic differences between natives and immigrants, and in particular in regard to refugees and asylum-seekers. As we have demonstrated in this study, the share of low-skilled among the non­EU28 immigrants in many of the studied countries is manyfold higher than for the natives. As the EU countries can be considered as knowledge-­ intensive economies, a significantly lower level of human capital will inevitably lead to a different level of performance for the group in question, relative to the natives. However, in the social sciences this difference between natives and immigrants is commonly explained by structural violence, where natives discriminate against and exploit the immigrants, and the solution lays in the abolition of the capitalist production system (Rauhut & Laine, 2023). Policy, in turn, is a function of ideologies and political realities. Although the distribution of resources may have been seen as more optimal from an economic point of view, ideological aspects play an important role in decision making (Lane & Ersson, 1998). However, it must be acknowledged that politicians and bureaucrats not only try to serve the ‘public interest’ but also their own. Being (re)elected is a key incentive in politics and is reflected in decision making (Tullock, 1976). Hitherto, the design of the policies in focus has been influenced by aspects other than economic concerns and empirical evidence, and this is a recurring problem. Political economy is also of relevance when it comes to the time dimension of the

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economic integration of immigrants in the new host communities. It is of utmost importance to have a realistic view of how long the economic integration of immigrants will take. There is no quick fix, and most immigrants (especially refugees and asylum-seekers) will not be economically integrated in one or two years; a time dimension up to ten years may be more realistic, and such a time dimension suggests adopting a long-term perspective. However, many politicians want to be (re)elected and are happy to propose measures that increase their chances of this happening, even though the proposals they put forward do not necessarily stimulate the economic integration of immigrants. A third area relates to the role of New Public Management (NPM) and its role in immigrant integration services. In line with the NPM ideology, public services should be produced with a business logic and they should be decentralised to largest possible extent. However, this local turn in immigrant integration services means that the economy of scale is lost, and many local actors re-invent the wheel. None of this is economically efficient (Kaya, 2023), and moreover, the implementation of NPM in the immigrant integration system produces a system which is fragmented, where the responsibility for the service, the financing of the service and the production of the services are separated. In many cases, different governance levels and policy areas contradict each other, which leads to inefficient outcomes (e.g., Kaya, 2023; Gruber & Rauhut, 2023; Scholten, 2016). Moreover, the market has a weaker position in the regions beyond the cities, and just because the market can produce integration services better than the public sector in the cities, it is bold to postulate that the same will happen in sparsely populated regions with a lower aggregate demand than in the cities (Gruber & Rauhut, 2023). The market holds a weaker position in these areas, and many remote and peripheral regions have difficulties to produce basic welfare services without government support (Borges et al., 2015). However, many of these regions are now supposed to integrate immigrants with the help of the market. Consequently, missing markets and market failure are legitimate causes for public intervention, and some of the not-so-favourable findings featured in this study may be related to the challenges with NPM outlined here. In regard to the scope of the presented study, while this book focuses on the EU, more comparative research covering other regions may help to reveal the effects of, for example, cultural patterns or the attitudes of citizens of the home country as well as migrants in this context.

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7.4  Conclusion Immigration, immigrants and the impacts of immigration on the host community are topics that most people have an opinion about. Many studies have already tried to identify the pros and cons of immigration. This volume should not be seen as a post in favour of or against immigration—rather, it should be seen as an attempt to clarify some facts and figures. Immigration is a reality which will continue to play an important role on the demographic development of the Member States of the European Union. The alternative to immigration is to close the borders to all non­EU countries, and we do not consider this as a realistic nor desirable policy option. However, to optimise the potential economic impact of immigration, and hence make immigration as economically beneficial as possible, we need to understand what happens in immigration and what the effects are in different types of territories when different types of labour is added to the already existing labour force. For politicians and practitioners, such knowledge is particularly important. This volume reflects on the challenges and potentials of migration beyond the cities, focusing not on a national level but a regional level that has so far often been neglected in comparative research on the effects of migration. While some of the dominant theoretical approaches hold well to explain the effects of high-skilled or low-skilled migration, suggesting which kind of migration would be needed (or not needed) to meet the demands of the labour market needs other theoretical approaches to be developed. Empirically, the evidence points at disturbing findings: immigration to regions beyond the cities and non-metropolitan regions, and especially that of low-skilled immigrants, will lead to more negative economic consequences for such regions, for example, in regard to higher unemployment, lower employment rates and lower economic growth. Moreover, the findings suggest that the more immigrants in regions beyond the cities, the fewer company start-ups arise. To address slow processes such as depopulation and population ageing in non-metropolitan regions, it is important that decision-makers and practitioners do not get stuck in simple headcount exercises. If a region, sector or branch lose x amount of labour due to, for example, retirement and out-migration, then the solution may not necessarily be to simply import the same number of immigrants. Unless the substitutability between immigrant and native labour is very high, matching problems will arise. In the long run, the negative effects of such immigration will

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overshadow the positive ones, especially in cases where the integration and potentially education was not successful. Societies, like their economies and economic structures, change over time (see, e.g., North & Thomas, 1973; Rosenberg & Birdzell, 1986). Industries that in the twentieth century were in a great need of labour (e.g., the manufacturing industry) are no longer the hub of the European countries’ economies. The industries of the future and the expansive industries are mainly to be found in the cities and place high demands on the human capital of the workforce (Chiswick, 2019). To replace relatively unqualified native labour in decaying economic sectors in non-­metropolitan regions with other unqualified labour (either natives or immigrants) will not contribute to productivity increases and economic growth. Furthermore, headcount exercises to replace ‘missing’ labour may be counter-productive in a long-term perspective, hindering structural change. Hence, the policy measures that are invoked must take this aspect into account. The overall conclusion of this volume is that there is a significant and important difference in the economic impact of immigration to regions beyond the cities relative to cities and metropolitan regions. The argument that immigrants and refugees can revitalise depopulating peripheral regions by adding labour and a relatively younger population to mitigate demographic ageing is often voiced. But to what extent these immigrants are profitable for the host societies depends on how quickly they enter the labour market and can provide for themselves. Accordingly, a net transfer from natives to immigrants when the immigrants cannot provide for themselves will make immigration costly. Moreover, depending on the scale being analysed, the economic consequences will differ. At one scale the consequences may be positive, while at another scale the consequences may be negative. For some single companies or regions, labour immigration may mitigate or even solve the bottlenecks in production, while other regions will be completely dependent on government subsidies and public transfers to provide for refugees who have been unable to enter the labour market. Furthermore, the time dimension of the effects of immigration must also be assessed. The short-term positive effects (i.e., a labour supply for companies) must be weighted against the risk of slowing down the structural change of the economy and the risk of getting caught in a vicious circle of underdevelopment. Short-term costs (net public transfers from natives to refugees) must also be weighed against any potential long-term

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gains, given that skilled immigrants can potentially mitigate bottlenecks on the labour market, and the labour force can be increased. The economic consequences of immigration are indeed multi-faceted. Hence, it is not possible to offer general statements such as that immigration is economically entirely positive or entirely negative. The positive consequences are most welcome, both from a short- and long-term perspective. However, the negative consequences of immigration cannot entirely be avoided, and both positive and negative consequences occur parallel. By understanding the multi-faceted character of the effects of immigration, we believe that the negative effects can be minimised and the positive effects maximised. A first step towards this is to understand that the economic impact of immigration beyond the cities differs from that seen in cities. Ultimately, the key question is to what extent there is a regional demand for labour, and if immigrants can offer a matching labour supply or at least meet the required qualifications regarding, for example, language proficiency and the validation of educations on a short-term basis (i.e., within 6–12 months). This will determine the economic effects of immigration and especially so for regions beyond the cities.

References Borges, L., Humer, A., & Smith, C.  J. (2015). Europe’s possible SIG futures: Territorial settings and potential policy paths. In H.  Fassmann, D.  Rauhut, E. Marques da Costa, & A. Humer (Eds.), Services of general interest and territorial cohesion: European perspectives and national insights (pp.  123–146). Vienna University Press. Chiswick, B.  R. (2019). Managing immigration in the 21st century. In B.  Elsner (Ed.), Foundations of migration economics (pp. 583–595). Oxford University Press. Gruber, M., & Rauhut, D. (2023). Immigrant integration in Austria and Sweden – A patchwork of multilevel governance and fragmented responsibilities. In J. Laine, D. Rauhut, & M. Gruber (Eds.), Assessing the social impact of immigration in Europe: Renegotiating remoteness. Edward Elgar. Kaya, A. (2023). Local turn in migrant integration practices of Turkey: Syrians in Bursa. In J. Laine, D. Rauhut, & M. Gruber (Eds.), Assessing the social impact of immigration in Europe: Renegotiating remoteness. Edward Elgar. Kordel, S., & Weidinger, T. (2018). Current Processes in Immigration to European Peripheries: Status Quo, Implications and Development Strategies. In S. Kordel, T. Weidinger & I. Jelen (Eds.), Processes of immigration in rural Europe: the status quo, implications and development strategies (pp. xv–xxx). Newcastle upon Tyne: Cambridge Scholars.

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Lane, J. E., & Ersson, S. O. (1998). Politics and society in Western Europe. Sage. North, D. C., & Thomas, R. P. (1973). The rise of the Western world. Cambridge University Press. Rauhut, D., & Laine, J. (2023). Theorising immigrant integration: A critical examination. In D. Rauhut (Ed.), New methods and theory on immigrant integration: Insights from remote and peripheral areas. Edward Elgar. Rauhut, D., & Rauhut Kompaniets, O. (2018). The impact of immigrant entrepreneurship on regional development in Western Sweden. Romanian Journal of Regional Science, 12(1), 18–42. Rosenberg, N., & Birdzell, L. E., Jr. (1986). How the West Grew Rich: The economic transformation of the industrial world. Basic Books. Scholten, P. (2016). Between National Models and Multi-Level Decoupling: The Pursuit of Multi-Level Governance in Dutch and UK Policies Towards Migrant Incorporation. Journal of International Migration and Integration, 17(4), 973–994. Schomaker, R. M., & Bauer, M. W. (2019). Alternative Mechanismen zur europaweiten (Um-)Verteilung von Flüchtlingen und Migranten. FÖV Discussion Paper 87. Speyer. Tullock, G. (1976). The vote motive. Institute of Economic Affairs.

Index1

A Ageing population, 7, 59, 83, 114, 126 Aggregate level, 33–40, 112 Agriculture, 6, 23, 55, 56, 65, 88, 99, 104, 128, 135 Apprenticeships, 137, 139, 157 Assimilation, 43, 112 Asylum-seekers, v, 1–4, 8, 73n4, 110, 112, 113, 126, 127, 130–132, 136, 138, 140, 156, 158, 160–162 Austria, 4, 10, 34, 56, 72, 85, 104, 105, 128 B Becker, G., 90 Belgium, 4 Birth rate, 80, 83

Borjas, G.J., 7, 8, 22, 24–27, 29, 30, 32, 33, 35, 38–43, 99, 127, 133, 134, 138, 143 Bulgaria, 10, 72, 83, 85, 87, 91, 94, 97, 98 C Canada, 128 Capital, 11, 23–34, 35n3, 37, 37n4, 40, 42, 54, 60, 61, 63, 64, 72, 74, 85, 89, 90, 99, 105, 124, 160 Causes of stay, 159, 160 Chiswick, B.R., 2, 7, 8, 25, 38, 40, 41, 127, 128, 132, 133, 138, 164 Christaller, W., 5, 65 Cities, v, 3–5, 8–12, 44, 51–67, 71, 73, 74, 79, 81, 83–115, 123, 124, 126, 134–137, 142, 155–160, 162–165

 Note: Page numbers followed by ‘n’ refer to notes.

1

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 D. Rauhut et al., The Economics of Immigration Beyond the Cities, https://doi.org/10.1007/978-3-031-30968-7

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INDEX

Company, 12, 37, 37n4, 42, 64, 66, 73, 80, 89, 92, 95, 96, 100–102, 101n2, 125, 133, 135, 157, 164 Company level, 61, 63 Company start-ups, 101–103, 156, 163 Competitiveness, 6, 12, 36, 41, 64, 124 Complementarities, 30 Consumption, 24, 28–29, 44, 63–66, 90, 96, 98–100, 114, 135, 143, 156 Culture, 23, 31, 103, 104, 137 D De-industrialisation, 6, 134 Demand-based educations, 139, 157 Demanded immigrants, 135 Demographic change, 3, 83, 85, 86 Depopulation, 135, 163 Deportation, 128n1 Discrimination, 93, 95, 134, 137, 158 Dispersal policies, 4, 113 Dual Labour Market Theory, 35n3, 40, 40n5 The Dublin Conventions, 130 Dustmann, C., 7, 10, 39–41 E Economic award, 159 Economic growth, 5, 34–37, 35n3, 61, 65, 67, 73, 87, 89, 90, 95, 100, 104–110, 114, 115, 123, 124, 156, 157, 163, 164 Education, 25, 31, 35n3, 52, 57, 59, 62, 80, 88, 90–96, 102, 105, 107–109, 112, 113, 129, 132, 137, 139, 143, 155, 156, 159, 164, 165

Educational attainment level, 66, 90–96, 105, 110, 129, 135, 157 Ekberg, J., 35, 38 Elasticities, 29 Employment, 5, 6, 26, 34, 36, 39, 41, 42, 55, 56, 59, 63, 80, 89–95, 102, 105, 106, 108, 111, 114, 125, 127, 128, 132, 135, 139, 141–143, 156, 157, 160, 163 Entrepreneurship, 42–43, 66, 73, 100, 101, 103, 114, 156, 157 EU-citizens, 9, 10, 71, 72, 80, 87, 91, 92, 94, 160 European Free Trade Association (EFTA), 3n1, 72 European Union (EU), 1–3, 3n1, 5, 10–12, 11n4, 34, 39, 52, 55–58, 57n1, 67, 71, 72, 72n3, 80, 81, 83–85, 91, 94, 96–98, 96n1, 124, 128–132, 135, 136, 140, 143, 159–163 Excess demand, 12, 32, 59–62 Excess supply, 4, 12, 61–65, 89, 106, 125, 135, 155, 156 External effects, 27, 28, 30 Externalities, 26, 37, 38, 42, 110, 134 F Family reunification, 3, 3n1, 126, 127 Fassmann, H., 4, 40 Finland, 4, 10, 39, 72, 83, 85, 87, 95, 100, 105 Fiscal effects, 110–113 Foreign Direct Investment (FDI), 5, 123 France, 1, 4, 84, 128 G Germany, 1, 2, 4, 10, 72, 85, 87, 97, 98, 105, 127, 128

 INDEX 

Government, 6, 7n2, 39, 57, 94, 96, 100, 110, 111, 113, 115, 125, 128, 130, 133, 134, 162, 164 Government level, 110, 115 Greece, 38 Gross domestic product (GDP), 5, 27, 33, 34, 36, 38, 39, 58, 73, 80, 88, 96, 104–106, 108, 111, 160 Gross value added (GVA), 80, 107, 108 H Hierarchy of places, 65 High-skilled labour, 65, 128 Housing, 3, 28, 43, 56, 65, 96, 98, 113, 114, 126, 132, 141, 158 Human capital, 7, 8, 34, 38, 61, 63, 64, 90, 95, 104, 110, 127, 129, 131, 132, 136, 143, 161, 164 I Immigrant settlement, 125–126 Immigration surplus, 26–30, 43, 67 Incentives, 7, 22, 23, 54, 127, 156, 158, 159, 161 Income, 8, 22–25, 27, 28, 36, 39, 40, 44, 53, 54, 57–59, 61, 63, 65, 66, 80, 96–100, 96n1, 110, 114, 134, 141, 156, 157, 160 Individual level, 12, 22, 34, 40–43, 61, 63, 64 Industry, 6, 35n3, 39, 54, 60, 64, 65, 80, 89, 164 Informal institutions, 137 Innovation, 5, 24, 31, 34, 42–43, 65, 66, 73, 100, 101, 103 Institutional change, 89 Integration, v, 7, 10, 11, 25–27, 56, 59, 60, 66, 67, 110, 113–115,

169

130, 131, 136, 137, 140–143, 156, 158, 159, 162, 164 Integration policy, 7, 10, 71, 139, 160 Integration process, 136, 137, 141 Introduction programmes, 129 Investments, 5, 37, 42, 55, 63, 96, 98–100, 112, 113, 123, 124, 136, 137, 143 Israel, 38, 41, 127 Italy, 1, 4, 10, 38, 72, 83–85, 87, 94, 100, 105 J Job-seeking, 141 K Knowledge-intensive economy, 90, 135, 136, 141, 161 L Labour demand, 11, 33, 60, 62, 89, 125, 136 Labour-intensive, 3, 35n3, 37, 42, 59, 63, 64, 88, 99, 100, 103, 104, 133 Labour market, v, vi, 4, 7, 8, 10, 12, 22–24, 27, 29, 31, 32, 35n3, 38, 40–43, 40n5, 52, 55–58, 61–66, 71, 80, 83–90, 92, 94–97, 102, 110–114, 126–129, 131, 132, 135–143, 155–161, 163–165 Labour market programmes, 129 Labour migrants, v, 1, 8–10, 21, 37, 38, 110, 126, 129, 134n3, 138, 143 Labour shortage, 7, 53, 59–61, 88–90, 92, 99, 100, 126, 127, 133, 139, 142

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INDEX

Labour supply, 25, 30, 59–61, 63, 85, 86, 164, 165 Language proficiency, 7, 41, 129, 136, 143, 157, 165 Language training, 129, 157 Lifestyle migration theory, 9 Long-term, 32, 34–36, 35n3, 37n4, 61, 89, 100, 110, 111, 133, 139, 140, 157, 162, 164, 165 Lösch, A., 5, 65 Low-productive, 3, 37, 59, 63, 88, 99, 100, 103, 104, 133 Low-skilled labour, 3, 23, 35, 65, 93, 114, 128, 135 M Manufacturing, 6, 65, 88, 99, 103, 104, 128, 135, 164 Market size, 27, 65 Marriage migration, 126 Massey, D.S., 21, 23 Matching, 5, 42, 61, 62, 89, 111, 125, 135, 141, 155, 158, 165 Matching problem, 89, 163 Medium-term, 37, 61 Metropolitan, v, 3, 11, 36, 55, 74, 79, 85–87, 90, 91, 95, 99, 101–106, 108–110, 113–115, 126, 155, 156, 158, 164 Migration, 1–4, 9, 10, 12, 21–44, 51–67, 74, 81, 83–87, 92, 104–110, 114, 115, 124–126, 132–135, 140, 155–157, 160, 161, 163 Migration flows, 1–4, 21, 22, 32, 34, 54, 58 Migration policy, 1–3, 34 Migration systems theory, 22, 54 Migration theories, 9, 57, 126, 156

Mismatch, 58, 64, 94, 95 Mobility Transition Theory, 23, 55 Motives for migration, 24 Mountainous, v, 6, 9, 10, 42, 44, 51, 52, 59–62, 65, 66, 72, 79, 90, 94, 95, 97, 100, 103, 104, 106, 113, 124–126, 132, 134, 142, 143 N Natural population growth, 84, 86 Neoclassical Migration Theory, 22, 53 The Netherlands, 4, 36, 85, 128 Networks, 22, 23, 37, 42, 43, 54, 57, 124, 125, 135, 136, 141–143 Networks Theory, 54 New Economics of Migration, 22, 54 Nijkamp, P., 8, 100 Non-metropolitan, 10, 11, 74, 79, 87, 90, 91, 95, 99, 101–108, 110, 114, 115, 134, 135, 155, 156, 158, 163, 164 Norway, 3n1, 4, 72, 72n3, 101n2 O Open Method of Coordination (OMC), 129, 130, 130n2 P Participation rate, 29, 73 Penninx, R., 137, 138 Peri, G., 8, 31 Piore, M.J., 22, 35n3, 40n5, 41, 59 Policy actors, 131, 133–134, 137 Policy design, 12, 131, 160 Poot, J., 6, 36, 100, 124 Portugal, 1, 2, 4 Production costs, 29

 INDEX 

Productivity, 5, 23, 27, 34, 36–38, 37n4, 41–43, 58, 61, 63, 65, 73, 87, 89, 90, 100, 104, 110, 126, 164 Pull factors, 21, 22, 54, 57, 61 Purchasing power standard (PPS), 80, 104–106, 108 Push factors, 161 Q Quota refugees, 132 R Racism, 137, 158 Rationalisation, 37–38, 64 Recognition of qualifications, 60, 62, 136, 142 Refugee crisis, 2, 128, 130, 143 Refugee reception, 7, 127, 131, 158 Refugees, v, 1–4, 3n1, 7–10, 21, 36–38, 41–43, 59, 61, 65, 73n4, 93–95, 110, 112, 113, 125–134, 134n3, 138–143, 156–158, 160–162, 164 Regional level, 12, 42, 57–59, 64, 80, 81, 86, 97, 113, 138, 140, 158, 160, 163 Remote, 5, 6, 9, 10, 42, 44, 51–62, 65–67, 72, 79, 90, 94, 95, 97, 100, 103, 104, 106, 113, 124–127, 132, 134, 136, 142, 143, 159, 162 Reporting country, 79, 80, 90, 155 Ruist, J., 39, 99, 110, 133, 138 Rural, v, 3–6, 9, 10, 23, 42, 51–57, 59–62, 64–67, 72, 74, 79, 83–91, 94, 95, 97, 100, 102–106, 110, 113, 124–127, 132, 136, 143, 159

171

S Scholten, P., 131, 138, 162 Seasonal jobs, 73 Segregation, 43 Service sector, 6, 65, 88, 99, 100, 128, 135 Short-term, 35, 35n3, 40, 58, 61, 89, 100, 110, 111, 139, 158, 164, 165 Social conditions, 8, 56 Socioeconomic mobility, 64 Soviet Union, 38, 41 Spain, 1, 4, 10, 38, 72, 85, 97, 98, 128 Spatial, 3–4, 9, 52–55, 57, 66, 74, 79, 81, 91, 104, 124, 134n3, 137 Stakeholders, v, 2, 11, 59, 73, 89, 97, 102, 104, 127 Stark, O., 8, 22, 35, 37, 39, 41, 42, 90, 133 STEM sector, 128 Straubhaar, T., 11, 11n4, 38, 39, 131 Structural change, 12, 37–38, 61, 63, 65, 89, 95, 100, 104, 133, 157, 164 Students, v, 2, 43, 81, 110, 126, 127, 138 Substitution, 29–31, 37n4 Sweden, 1, 2, 4, 10, 35, 37–39, 42, 56, 72, 85, 86, 96, 98, 104, 113, 127, 128, 132 T Tax, 2, 7n2, 12, 25, 30, 38–40, 61, 63, 64, 100, 110, 113, 134 Technological change, 89 Temporary migrant workers, 7 Theory of cumulative causation, 23, 55

172 

INDEX

Third Country National (TCN), 11, 57, 71, 73, 80, 86, 87, 90–100, 103–110, 123, 135, 141, 160 Tied movers, v, 3, 110, 126, 132, 138 Turkey, 2, 38, 72n3 U Ukraine, 2, 128, 130, 143, 158 Unemployment, 12, 25, 34, 39–42, 54, 58, 62–64, 73, 80, 92–95, 97, 100, 105–107, 114, 125, 156, 157, 163 UNHCR, 1, 3, 3n1, 132 United Kingdom (UK), 1, 3n1, 38, 39, 55 United States (US), 8, 9, 27, 32, 34, 35, 38–43, 59, 127, 129 Urban, 3, 5, 11, 23, 42, 51–57, 65, 66, 74, 79, 124, 134 Urbanisation, 3, 5 Urban-rural divide, 51–52

V Validation, 95, 165 The vicious circle of regional under-­ development, 64 W Wages, 8, 12, 21, 22, 22n2, 24, 25, 27–34, 36, 37, 37n4, 40–43, 40n5, 54, 59–62, 64, 89, 99, 127, 160 Welfare, 2, 7, 7n2, 25–27, 30, 38, 40, 43, 57, 64, 89, 110, 111, 113, 125, 126, 130, 133, 134, 162 Welfare dependency, 12, 97, 111, 125, 128 Welfare schemes, 63 Working-age population, 86–89 World systems theory, 23 Y Yugoslavia, 38