Optimal Currency Areas and the Euro, Volume II: Capital and Labor Mobility 3031388666, 9783031388668

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Optimal Currency Areas and the Euro, Volume II Capital and Labor Mobility

Peter Alfons Schmid

Optimal Currency Areas and the Euro, Volume II

Johannes Kabderian Dreyer · Peter Alfons Schmid

Optimal Currency Areas and the Euro, Volume II Capital and Labor Mobility

Johannes Kabderian Dreyer ISE Roskilde University RUC Roskilde, Denmark

Peter Alfons Schmid FOM University of Applied Sciences Munich, Germany

ISBN 978-3-031-38866-8 ISBN 978-3-031-38867-5 (eBook) https://doi.org/10.1007/978-3-031-38867-5 © The Author(s), under exclusive license 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. Cover illustration: © Melisa Hasan This Palgrave Pivot imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

In the first volume of Optimal Currency Areas and the Euro, we discussed the importance of business cycle synchronization for the well-functioning of a currency. The analysis was inspired by the theories of Mundell (1961), McKinnon (1963), and Kenen (1969). The Euro-Area (EA) is frequently criticized for lacking synchronization of the business cycles of its members. In our analysis, we showed that this was not really verified for a sample post-Euro introduction. On the contrary, these were more synchronized than one would expect, indicating an endogenous development of the common currency toward more synchronization. In this new volume, we discuss the second theoretical condition for optimal currency areas: factor mobility. We leave the topic of fiscal federalism, the third condition, to the third and final volume of our series. So, this pivot discusses the importance of migration and interregional capital flows for the success of a currency union. We analyze in detail how these factors work in the EA and to what extent they can counteract economic shocks with a sample starting with the introduction of the Euro until 2021. We also take into consideration how factor mobility has changed over time in the EA. To do so, we start with a literature review that is followed by applied descriptive and econometrical analysis. Our readers can thus use this pivot as a handbook for EA economics and factor mobility. The main audience for this series comprises scholars and advanced students in the fields of macroeconomics, econometrics, and management. They can use our contribution as reference for their v

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PREFACE

own research. Advanced graduate students could also use the pivot series in class to learn static and dynamic econometric methodologies based on our empirical work. Although not necessarily restricted to these, examples for potential target courses are Public Economics, European Economics, Applied Econometrics, and Quantitative Methods in Business Studies and Economics. Finally, policymakers get an overview of the main arguments in the optimal currency area discussion that can contribute to their work. Roskilde, Denmark Munich, Germany

Johannes Kabderian Dreyer Peter Alfons Schmid

References McKinnon, R. (1963). Optimum Currency Areas. American Economic Review, 53(4), 717–725. Mundell, R. (1961). A Theory of Optimum Currency Areas. American Economic Review, 51(4), 657–665. Kenen, P. (1969). The Theory of Optimum Currency Areas: An Eclectic View. In R. Mundell & A. Swoboda (Eds.), Monetary Problems of the International Economy. University of Chicago Press. Acknowledgments We would like to thank two anonymous scholars who contributed with their knowledge und rigor to this book.

Contents

1 2 5 6

1

Introduction 1.1 The Main Objectives of the Pivot 1.2 Key Findings References

2

Literature and Theory 2.1 Integrated Labor Markets 2.2 The EU Labor Market 2.3 Adverse Shocks and Adjustments in Labor Markets 2.4 Research Questions: Labor Mobility in Europe 2.5 The EU Capital Markets 2.6 Adverse Shocks and Adjustments in Capital Markets 2.7 Research Questions: Capital Mobility in Europe References

9 10 12 17 23 28 30 32 34

3

Labor Mobility, the Empirics 3.1 Labor Mobility: Methodology of Analysis 3.2 Descriptive Analysis 3.3 A Closer Look into Migration Flows 3.3.1 Net Migration Flows—Country vs. EU Region 3.3.2 Total Migration Flows—Country vs. EU Region 3.3.3 Net Migration Flows—Country vs. Country (Bilateral Relations)

39 39 43 49 49 53 55

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3.3.4

Total Migration Flows—Country vs. Country (Bilateral Relations) 3.4 Discussion References 4

5

Capital Mobility, the Empirics 4.1 Capital Mobility: Methodology of Analysis 4.2 Descriptive Analysis 4.3 A Closer Look at Capital Flows 4.3.1 Net Capital Mobility—Country vs. Country (Bilateral Relations) 4.3.2 Total Capital Flows—Country vs. Country (Bilateral Relations) 4.4 Discussion References Conclusion References

61 63 73 75 76 79 80 82 84 98 99 101 104

Appendix 1: Summary of Labor Mobility Literature

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Appendix 2: Summary of Capital Mobility Literature

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Appendix 3: Data

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Appendix 4: Cook’s Distance

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Reference

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Index

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About the Authors

Johannes Kabderian Dreyer earned his bachelor’s degree in Economics at the Pontifical Catholic University of Rio de Janeiro (Brazil) in 2004. He completed his master’s degree in Business Finance at the same university in 2007, financed by the National Council for Scientific and Technological Development (CNPQ). In 2011 he defended his doctorate in Financial Economics in Bavaria at the Ingolstadt School of Management (Catholic University of Eichstätt-Ingolstadt, KU), financed by the German Academic Exchange Service (DAAD). Today, he is Associate Professor of Financial Economics at Roskilde University. Peter Alfons Schmid earned his diploma degree (equivalent to a master’s degree) at the Catholic University of Eichstätt-Ingolstadt (KU) in 2004. He worked as a professional at global and regional accounting firms. During his doctoral studies, he was teaching and research assistant at the Chair of Economic Theory of the KU, where he earned his doctorate in 2011. After some years as postdoc, he founded and developed a tech start-up for professional services. Today he is Professor for business administration, especially finance and entrepreneurship, at the FOM University of Applied Sciences, and contract Lecturer at the Free University Bolzano-Bozen.

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List of Figures

Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 2.5 Fig. 2.6

Fig. 2.7

Fig. 2.8 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 4.1

Real wages before migration (Source Krugman & Obstfeld, 2009, p. 156) Migration and real wage adjustment (Source Krugman & Obstfeld, 2009, p. 156) Unemployment rates (in %) in the four largest EA economies (Source ESTAT) Labor force participation rates (in %, 20–64 years) in the four largest EA economies (Source OECD) Ratio of nationals (15–64 years) working abroad in other EU/EFTA countries (Source ESTAT) Nominal wages (industry, construction, services, no public admin, defense, social security) in the four largest EA economies (Source ESTAT) Real wages (industry, construction, services, no public admin, defense, social security) in the four largest EA economies (Source ESTAT, own calculations) Capital mobility and adjustment to differing interest rates (Source Krugman & Obstfeld, 2009, p. 156) Intra- and total migration flows of EU countries over time Volatilities of intra- and total EU migration and their relation over time Per capita GDP growth and unemployment vs. volatility of intra-EU migration Lag per capita GDP growth and lag unemployment vs. volatility of intra-EU migration Bilateral capital mobility and its volatility over time (EU countries)

10 11 13 15 20

21

22 30 45 46 47 48 81 xi

List of Tables

Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5

Combination of relative independent variables for multiple regression estimates N et Mig Simple regressions—dependent variable: pop i,t i,t N et Mig Multiple regressions—dependent variable: pop i,t i,t V ol Mig Simple regressions—dependent variable: pop i,t i,t

Table 3.9 Table 3.10

57

Multiple regression Model C—dependent variable: 58

Multiple regression Model D—dependent variable: V ol Mig i,t popi,t

59

N et Mig i, j,t Simple regression—dependent variable: popi,t

60

Multiple regressions of Models A to D—dependent variable:

Table 3.11

54 56

V ol Mig i,t popi,t

Table 3.8

52

Multiple regression Model B—dependent variable: V ol Mig i,t popi,t

Table 3.7

50

Multiple regression Model A—dependent variable: V ol Mig i,t popi,t

Table 3.6

43

N et Mig i, j,t popi,t

V ol Mig i, j,t Simple regression—dependent variable: popi,t

62 64

xiii

xiv

LIST OF TABLES

Table 3.12

Multiple regression Model A—dependent variable: V ol Mig i, j,t popi,t

Table 3.13

Multiple regression Model B—dependent variable: V ol Mig i, j,t popi,t

Table 3.14

Table 4.1 Table 4.2

V ol Mig i, j,t popi,t

71

N et Del Lia Simple regression—dependent variable: G D PU S i, j,t i,t

80

Multiple regressions of Models A to D—dependent N et Del Liai, j,t G D PU Si,t

V ol DelCap Simple regression—dependent variable: G D PU S i, j,t i,t

89 92

Multiple regression Model D—dependent variable: V ol DelCapi, j,t G D PU Si,t

Table A.1 Table A.2

86

Multiple regression Model C—dependent variable: V ol DelCapi, j,t G D PU Si,t

Table 4.7

85

Multiple regression Model B—dependent variable: V ol DelCapi, j,t G D PU Si,t

Table 4.6

83

Multiple regression Model A—dependent variable: V ol DelCapi, j,t G D PU Si,t

Table 4.5

69

Multiple regression Model D—dependent variable:

variable: Table 4.3 Table 4.4

67

Multiple regression Model C—dependent variable: V ol Mig i, j,t popi,t

Table 3.15

65

Summary of the literature on labor mobility Summary of the literature on capital mobility

95 105 111

CHAPTER 1

Introduction

Abstract This chapter introduces the pivot—the second of a series of three on optimal currency areas and the Euro. The introduction describes the main objectives of the pivot, which is dedicated to the analysis of “factor mobility,” and also briefly summarizes the results of our research. Keywords Euro · Optimal Currency Areas · Factor mobility · Labor mobility · Capital mobility

According to the theory of optimal currency areas (OCA) of Mundell (1961), McKinnon (1963) and Kenen (1969), three criteria should be met for the well-functioning of a currency union: (1) synchronization of the business cycles of member countries, (2) perfect factor mobility, and (3) a risk-sharing mechanism. The first criterion is rarely met in the real world. Different regional economies might share the same business cycle but at the same time experience different intensities of economic up- and down-swings. In addition, larger integrated economies, like the United States or the European Union, consist of quite different regional parts that might be hit by asymmetric shocks. In the United States, the Northeast, the Middle Atlantic, the Rust Belt, the Sun Belt, and the West Coast are economically integrated yet differ considerably. In © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kabderian Dreyer and P. A. Schmid, Optimal Currency Areas and the Euro, Volume II, https://doi.org/10.1007/978-3-031-38867-5_1

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the Euro-Area (EA), there are differences between the core countries (Germany, Austria, the Netherlands), the Mediterranean (Italy, France, Spain), and peripheral countries like Ireland and Finland. Even within some of these countries one might observe major misalignments. For example, Northern Italy, especially the Trentino-Alto Adige region, does not only differ in its income level from the Mezzogiorno region, but also in its socio-economic structure. In contrast to prior literature, the first pivot of this series (Dreyer & Schmid, 2020) showed that business cycles across EA countries are today even more synchronized than in the United States and that synchronization increased over time. Nevertheless, economic shocks are not equal for all regions of the EA. Business cycle correlation is considerably below one in times of economic crisis. Consequently, there is a call to analyze the second and third criteria of the OCA theory in the EA context. They are of practical and academic relevance. This second pivot of our series is dedicated to the analysis of the second OCA criterion: factor mobility. We use a sample that ranges from the introduction of the Euro until 2021.

1.1

The Main Objectives of the Pivot

With flexible exchange rates and national interest rates, there are two adjustment mechanisms in the case of disparity of intensities in economic cycles or idiosyncratic shocks. Economies that do not share a common currency have the advantage that the adjustment of exchange rates and national interest rates work as automatic tools to counteract economic shocks. Once a regional economy is hit differently by an economic shock— e.g., harder or by a negative asymmetric shock—a devaluation of the national currency reduces the real wage level in the short run and keeps the regional economy competitive. Structural unemployment does not rise as much as in the case of a regional economy that has rigid nominal wages and lacks sufficient interregional labor mobility. As this short-term devaluation mechanism is not feasible with fixed exchange rates, a stronger labor mobility is needed among countries that share a common currency. Thus, migration acts as a stabilization force against economic shocks. For example, when a country faces a recession, unemployment increases. Natural adjustments to this problem would be a decrease in real wages or simply migration. Now, if migration is low and monetary policy cannot be customized to a particular country, the

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recession will be prolonged with a higher unemployment rate than necessary. This creates a constant negative pressure on real wages until a new natural equilibrium on the labor market is reached. Usually, one tends to use examples of recessions to explain this phenomenon. However, an analogous reasoning can be made for the case of economic booms. With strong growth, unemployment rates tend to decrease below natural levels, so that wages are expected to increase and workers to come from other regions to cover the lack of labor supply. Capital mobility is seen as another adjustment mechanism in currency unions (Ingram, 1959). In a world with perfect capital mobility, domestic investment does not depend on domestic savings (Stirböck & Heinemann, 1999). Thus, shocks on domestic savings should not matter for domestic investments. The capital inflow of foreign savings would counterbalance the lack of domestic ones. This mechanism should avoid adverse reactions of economic activity due to economic shocks, with capital flowing to greater business opportunities in the affected countries. In a nutshell: Given the absence of flexible exchange rates, a currency union needs other adjustment mechanisms for the absorption of differing economic shocks between regions (Feldstein, 1997). For example, in the United States, this role is played by factor mobility, where labor and capital mobility ensure the well-functioning of the US dollar in the advent of such shocks. It is the first objective of this second pivot of our series to discuss why factor mobility is so important for the well-functioning of currency unions. But we must also ask ourselves: How easy is it to put factor mobility in practice? In the literature from the 1990s and the early years of the EA, we find plenty of skepticism on whether the currency union offers enough factor mobility to deal successfully with economic challenges, especially when it comes to labor mobility. Part of this skepticism is especially related to the observation of historically low migration within the EA. In the words of Martin Feldstein (1997): As long as Europeans speak ten different languages, cross-border movement in response to job availability will be far less than movement among American regions. (…) While Americans don’t hesitate to move from Ohio and Massachusetts to Arizona and California, Germans are loathe to leave one part of Germany for another. When it comes to capital mobility, the European Union experienced a deeper financial integration even before the introduction of the Euro.

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Capital controls were abolished, and financial regulations were harmonized (Lane, 2013). In addition, the Euro removed the currency risk for member countries. As a result, obstacles to capital mobility decreased considerably within the EA in comparison with other advanced economies (Lane, 2009; Lane & Milesi-Ferretti, 2008). This, in theory, should be a sign of financial openness, which is usually associated with lower liquidity risk and transaction costs as well as higher bank competition1 and economic growth (Alizadeh et al., 2021). But despite the spectacular boom of capital flows in the early years of the Euro, capital mobility proved to act in a destabilizing way during the financial crisis. Moreover, capital flows generated neither real income nor productivity convergence, according to Franks et al. (2018). The EA experienced a boom-bust cycle that was larger than similar cycles observed in the broader European region and in the global set of advanced economies (Lane, 2013). In other words, when it comes to factor mobility, challenges in the EA are not only associated with labor movement. There are a few issues related to capital mobility, too. It is the second objective of the pivot to discuss major shortcomings of the EA with respect to factor mobility as an economic stabilizer. We do so based on the literature. As a third objective of this pivot, we also want to investigate empirically the economic stabilization role of factor mobility in the EA. Did the EA endogenously develop a better functioning of this stabilization mechanism since its beginning? As a variable to proxy labor mobility, the literature commonly uses migration rates between regions. On the other hand, no consensus exists for capital mobility. Owing to data limitations, it is difficult to directly measure capital flows (Böwer & Guillemineau, 2006). Thus, in our descriptive analysis, we use bilateral and aggregate migration rates to study labor mobility. On the other hand, to study proxy capital mobility, we collected the changes in claims and liabilities of each sampled country in another from the Bank for International Settlements (BIS). By doing so, it is our aim to offer the reader a descriptive analysis of labor and capital flows in the EA. We further build models where these flows are explained by economic variables, such as relative economic growth, unemployment, social benefits, and wealth. We will apply panel regressions to estimate our models and reflect upon how migration and 1 Higher bank competition can lead to higher economic stabilization as it dampens systemic risk (Dreyer et al., 2018; Leroy & Lucotte, 2017).

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capital movement counteract economic shocks in the EA. The quantitative analysis will also show whether factor mobility has been increasing during the Euro era.

1.2

Key Findings

Our findings on labor mobility indicate: • A strong increase in migration of workers within EU countries in our sample period (after the introduction of the Euro). Relatively, intraEU migration increased more than migration overall, supporting the argument of an endogenous development of the currency union toward an OCA. • That unemployment has a direct relationship with migration flows in the EA. • That in the EA, migration also acts as a redistribution mechanism as it correlates with wealth differentials between countries. • That EA membership is associated with higher intra-EU migration. Generally, the literature diverges in relation to the unemploymentstabilizing role of migration as well as in fostering wealth distribution in the EA. We can argue that our findings reconcile the literature by showing the dependency of results on the sample period selected. In other words, it would be legitimate to be skeptical about migration before the introduction of the Euro. However, this is not the case when considering the post-introduction period. Here we observe an endogenous development in labor mobility toward the standards required by the well-functioning of an OCA. Our findings on capital mobility indicate that: • Relative unemployment, wealth, and social spending explain a small part of capital flows. • Home bias may exist in capital investments, as there is a preference of European investors to keep their assets in EA member countries. However, we could argue that in line with the literature, we faced data restrictions that to some extent limit our conclusions on capital flows.

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The pivot is structured as follows: Chapter 2 discusses both theory and literature on labor and capital mobility, with reference to Mundel’s OCA criteria. Chapters 3 and 4 offer an empirical analysis of labor and capital mobility, respectively. Each of the two chapters includes a descriptive statistical analysis, followed by an estimation of the models and a subsequent discussion of the implications of the results on the EA. Chapter 5 concludes the pivot with policy recommendations.

References Alizadeh, S., Shahiki Tash, M. N., & Dreyer, J. K. (2021). Liquidity risk, transaction costs and financial closedness: Lessons from the Iranian and Turkish stock markets. Review of Accounting and Finance, 20(1), 84–102. Böwer, U., & Guillemineau, C. (2006). Determinants of business cycle synchronization across Euro-area countries. (ECB Working Paper Series, No. 587). Dreyer, J. K., & Schmid, P. A. (2020). Optimal currency areas and the Euro, Volume I: Business cycles synchronization. Springer Nature. Dreyer, J. K., Schmid, P. A., & Zugrav, V. (2018). Individual, systematic and systemic risks in the Danish banking sector. Czech Journal of Economics and Finance, 68(4), 320–350. Feldstein, M. (1997). The political economy of the European economic and monetary union: Political sources of an economic liability. Journal of Economic Perspectives, 11(4), 23–42. Franks, J. R., Barkbu, B. B., Blavy, R., Oman, W., & Schoelermann, H. (2018). Economic convergence in the euro area: Coming together or drifting apart? (IMF Working Paper No. 18/10). Ingram, J. (1959). State and regional payments mechanisms. The Quarterly Journal of Economics, 73(4), 619–632. Kenen, P. (1969). The theory of optimum currency areas: An eclectic view. In R. Mundell & A. Swoboda (Eds.), Monetary problems of the international economy (pp. 41–60). University of Chicago Press. Lane, P. R., & Milesi-Ferretti, G. M. (2008). The drivers of financial globalization. American Economic Review, 98(2), 327–332. Lane, P. R. (2009). EMU and financial integration. In B. Mackowiak, F. Mongelli, G. Noblet, & F. Smets (Eds.), The euro at ten: Lessons and challenges (pp. 82–115). European Central Bank. Lane, P. (2013). Capital flows in the euro area. (European Commission Directorate-General for Economic and Financial Affairs Economic Papers No. 497).

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Leroy, A., & Lucotte, Y. (2017). Is there a competition-stability trade-off in European banking? Journal of International Financial Markets, Institutions and Money, 46, 199–215. McKinnon, R. (1963). Optimum currency areas. American Economic Review, 53(4), 717–725. Mundell, R. (1961). A theory of optimum currency areas. American Economic Review, 51(4), 657–665. Stirböck, C., & Heinemann, F. (1999). Capital mobility within EMU . (ZEW Discussion Papers, No. 99).

CHAPTER 2

Literature and Theory

Abstract This chapter analyzes different theories on how factor mobility can counteract both symmetric and asymmetric economic shocks in currency unions, as well as states the research questions of this pivot. Moreover, it gives the reader a summary of the literature on labor and capital mobility, highlighting their importance to the well-functioning of currency unions, where countries are not able to execute their own monetary policies by themselves. The chapter also discusses the possibility of endogenous development of the EA toward more factor mobility over time. Keywords Factor mobility theories · Optimal Currency Areas · Migration and capital flows · Economic shocks · Economic stabilization

Our first pivot (Dreyer & Schmid, 2020) discussed business cycle synchronization in light of the criteria of optimal currency areas (OCAs). This chapter presents theories, literature, and stylized facts on factor mobility.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kabderian Dreyer and P. A. Schmid, Optimal Currency Areas and the Euro, Volume II, https://doi.org/10.1007/978-3-031-38867-5_2

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2.1

Integrated Labor Markets

For the case of simple international and integrated labor markets, spatial mobility results from economic incentives. Consequently, adjustment mechanisms lead to real wage convergence (see Krugman & Obstfeld, 2009, pp. 154–160). Assume, for example, two regions with different relative labor supplies: Whereas region A has initially relative labor of O A L 1 , region B has O B L 1 . Region A shall be relatively richer in labor supply. Figure 2.1 shows these endowments together with the respective marginal products of labor FLA and FLB , where F is the regional production function: The marginal product increases with decreasing relative labor supply as an increasing number of workers decreases productivity for the latest labor unit given fixed capital supply and other resources such as land. Consequently, labor in region A attracts a lower real wage ω A , which is equal to the marginal product of labor. In region B, the real wage ω B is higher owing to scarcer labor in relation to other production factors. Total product is equal to the area below the marginal labor product in both regions. The share of labor income of total income is larger in region

Fig. 2.1 Real wages before migration (Source Krugman & Obstfeld, 2009, p. 156)

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LITERATURE AND THEORY

11

B in comparison with region A: The relation between the area L 1 Y ω B O B and the total area below the curve FLB to the right of L 1 —which is relative labor income in region B—is larger than the relation between the area O A ω A Z L 1 and the total area below the FLA curve to the left of L 1 , i.e., relative labor income in region A. The higher real wage in region B is an incentive for workers in region A to emigrate from A to B. The adjustment mechanism is shown in Fig. 2.2. Only for the intersection X of marginal labor products in both regions, this incentive stops to exist. Real wages will have converged to the interregional real wage rate of equilibrium ωe and relative labor supplies adjusted to O A L 2 in region A and O B L 2 in B. Interregional migration is shown by O A L 2 − O A L 1 (net emigration from region A) and O B L 2 − O B L 1 (net immigration to region B). There is even an aggregate welfare gain as aggregate production in both regions increases by the area X Y Z . The production loss in region A equals the area L 2 X Z L 1 . It is overcompensated by the production gain in region B that equals the area L 2 X Y Z L 1 . On the individual level, there are losses. Workers in region A win; the owners of other (immobile) factors lose. Vice versa in region B.

Fig. 2.2 Migration and real wage adjustment (Source Krugman & Obstfeld, 2009, p. 156)

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Such an adjustment only works properly in competitive economies. Frictions like immigration restrictions impede full adjustment. Nevertheless, there is empirical evidence for real wage convergence by migration flows: • During the era of mass migration from the late nineteenth century until the 1920s from European countries to the overseas destinations in Northern America, Oceania, and Argentina. These were the main destinations for workers from Northern Europe. Real wages grew much slower in the destination countries than in the origin countries, leading to closing gaps between the levels of real wages (see Krugman & Obstfeld, 2009). • Recently, East–West migration in the European Union after the Eastern Enlargement in 2004 increased relative labor supply in the Western European economies. Real wages in Eastern European economies, consequently, started to converge, but on a relatively slow path (see Dorn & Zweimüller, 2021). This goes line with Fahri and Werning (2015) who show analytically in a general equilibrium model that there are welfare gains for movers, but only little impact on stayers in case of internal demand imbalances.

2.2

The EU Labor Market

The EU and EA labor markets show larger spreads in unemployment rates than their US counterpart—a clear sign for a less integrated European labor market (inter alia Dorn & Zweimüller, 2021). For the four largest EA economies that have a high market integration, unemployment ranges between 3.6% in Germany and 14.8% in Spain. Yet, labor mobility is still low between them (Mongelli, 2008). These are the lowest value and highest value for non-island countries. Although the general time trend for employment was recently the same, absolute levels in unemployment are quite different. For Italy and Germany, whose unemployment rates were similar in the 2010s, the gap in 2021 is 5.9% (see Fig. 2.3). In general, unemployment rates are higher in southern EU member countries than in core and northern EA member countries. Labor force participation rates (the proportion of the population aged 20 to 64 years that is economically active) vary from just above 70% in Italy to about 85% in the Netherlands and almost 87% in Estonia (levels in

2013

2014 Germany

2015

France

2016

2017 Italy

2018

Spain

2019

Fig. 2.3 Unemployment rates (in %) in the four largest EA economies (Source ESTAT)

0. 2012

5.

10.

15.

20.

25.

30.

2020

2021

2 LITERATURE AND THEORY

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2022, source: OECD). Figure 2.4 shows the development across the four major member countries and shows increasing labor force participation. In the United States the labor force participation rate has been around the same level in the last decade, oscillating around 77%, in 2022 it was 77.4% (source: OECD). But why are there such large disparities in unemployment and labor force participation in the EA? Formally, the pillars of the EU common market—known as the four freedoms—include the free movement of labor. Article 45 TFEU (Treaty on the Functioning of the European Union) clearly states that “freedom of movement for workers shall be secured within the Union,” including to “move freely within the territory of member states.” Limitations are, however, “justified on grounds of public policy, public security or public health.” In addition, the right of free labor mobility does not apply to “employment in the public service”—a sector responsible for roughly 15% of employment in the EA (source Eurostat). Despite general free labor mobility, five main reasons prevent an integrated labor market in the EU that could be comparable to the United States (see Dorn & Zweimüller, 2021 for a detailed discussion). First, in the United States, American English is shared as the mother tongue by most residents across single states. There are Spanish-only speakers, but bilingualism is increasing in the Hispanic community. Other “only” speakers (besides American English) do not play a role on balance. Although most Europeans learn English as a second language, their fluency is not sufficient for labor market participation. In many cases, English is not the business language, and even in cases where it is, applicants often don’t have adequate communication capabilities in English. Except in Ireland and Malta, English is not the language spoken at home by virtually all households. Three main working languages—English, French, and German—on the EU level, and in total 24 official languages, show that there is a language barrier in the EU labor market (Dreyer & Graversen, 2014; Krugman & Obstfeld, 2009). Even among academics, an area where English is supposed to be the most important language, knowledge in the appropriate regional languages is de facto mandatory in most member states. Only the Scandinavian countries might be an exception, as in Northern Europe English serves the role as a second language. Second, cultural distances are large in the EU in comparison with the United States. Even within countries, especially those with

Fig. 2.4 Labor force participation rates (in %, 20–64 years) in the four largest EA economies (Source OECD)

2 LITERATURE AND THEORY

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more languages, like non-EU Switzerland, interpersonal behavior varies broadly—for example, between the German- and French-speaking parts of the Alpine country. Local and regional practices are different between the countries with German, Latin, and Slavic heritage. In a nutshell, there is no unique European culture. One might object that even in the United States, the ways of doing business are different between the West and the East Coast, the Old South, and the Mountain states as well. However, these US regions share the same language, a common federal government, and a common belief in the pursuit of happiness—the American way of life. As example for cultural distances, even the most leftist democrats do not question nuclear energy in the United States. Across EU countries, on the contrary, nuclear energy evokes dissent, even within the same political family. Third, the labor market performance of immigrants, ceteris paribus, usually falls behind natives’ performance in EU countries. Even Italians moving to Germany do worse than Germans. The Nordic countries, proud of their egalitarianism, do not offer the same career paths and speed to immigrant workers. This is not different even for highly qualified workers.1 In the United States, nobody asks about one’s regional roots but, out of curiosity, is eager to learn from one another. Fourth, discrimination might explain the relatively poor labor market performance of immigrants. Anti-immigrant attitudes presumably were decisive for the unexpected Brexit vote in 2016. Although immigrants from Southern and Eastern Europe are not considered as “threatening” as immigrants from outside the EU, there are minor disadvantages associated with nationality. Fifth, even regional labor markets are inflexible, which reduces mobility within single countries. Of course, such labor market sclerosis has an impact on foreigners seeking access to the national labor markets. Efficiency wages and insider rights prevent wage dynamics that can lead to full employment. Sixth, there are institutional obstacles. Whereas the Bologna Process standardized higher education, occupational training is regulated on the national level. As consequence, there are obstacles to recognition. In general, access to professional work that is licensed nationally is complicated for foreign-trained workers. A second institutional drawback 1 A specific analysis for highly qualified immigrants and their job opportunities in Denmark is offered by Dreyer et al. (2014).

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concerns social security provisions, which are also regulated nationally. Social security not only varies substantially across countries, but also, minimum periods of national contribution prevent mobility as they result in workers not willing to “lose” previous contributions. In many cases, it is unclear whether entitlements can be transferred to a new country of residence. For all these reasons one could say that: “Labor mobility across European countries is (..) likely to remain lower than labor mobility across U.S. states. (…) [S]hocks will have larger and longer lasting effects on relative unemployment in Europe” (Blanchard & Katz, 1992).

2.3 Adverse Shocks and Adjustments in Labor Markets We should first consider inelastic regional labor supply. In such a case, adjustments only take place via relative wages in the short run. Lower labor demand decreases the level of relative wage. In the long run, however, labor supply decreases as a response to lower relative wages and labor demand recovers owing to the inflow of firms from other regions attracted by “cheaper labor.” Thus, “shocks to labor demand first lead to movements in relative wages and unemployment” which “trigger adjustments through both labor and firm mobility, until unemployment and wages have returned to normal” (Blanchard & Katz, 1992, pp. 2–3). Based thereon, Blanchard and Katz (1992) developed a model for regional labor markets and asked about the implications of mobility on labor supply, wages, and unemployment as a response to regional economic shocks. Their main assumptions are simple: (1) The various regions produce different bundles of goods and services for the common integrated market, just like in the EU; and (2) labor and capital are mobile in the long run. That is not necessarily the case, either in the EU or in the EA. Adverse labor demand shocks in one region increase unemployment and decrease wages. Consequently, some workers leave the region in search of greener grass. Wages start to increase again, but employment is lower than before. Positive shocks of labor demand in one region increase wages, inducing immigration from other regions but at the same time some relocations of firms that cannot afford the higher wages. Overall, Blanchard and Katz’s (1992) model is based on four equations, where

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region-specific variables are measured relative to their aggregate counterpart across all regions. Taking the logarithms of these fractions allows us to express the variables as simple differences. The four equations are:   ∗ ωi,t = −d n i,t − u i,t + z i,t (2.1) cωi,t = −u i,t

(2.2)

∗ ∗ s n i,t+1 − n i,t = bωi,t − gu i,t + xs,i + εi,t+1

(2.3)

d z i,t+1 − z i,t = −aωi,t + xd,i + εi,t+1

(2.4)

where ωi,t is the relative wage in region i at time t (i.e., the difference in the logarithms of the regional wage and the aggregate counterpart, u i,t ∗ is a region’s relative labor force). The is relative unemployment, and n i,t ∗ − u —is difference between the labor force and unemployment −−n i,t i,t employment. z i,t is the position of the labor demand curve. xs,i and xd,i are drift terms allowing for regional trends in labor supply and demand due to other reasons, like sunny weather and a region’s bundle of taxes and social expenditures in the case of labor supply. The non-wage regional amenities for firms are captured by the labor demand drift term. In addis d and εi,t+1 are white noises for both regional labor supply and tion, εi,t+1 demand, respectively. Equation (2.1) states that with constant employment, movements in z translate into movements in ω. This is the short-run insight from a labor market with inelastic labor supply and full employment. Allowing for changes in employment offers the possibility of two effects of changing regional labor demand: lower wages and employment in times of lower demand. Equation (2.2) states that higher relative unemployment leads to lower relative wages with semi-elasticity 1/c. Equation (2.3) formalizes changes in the regional labor force. Contrary to higher relative unemployment, higher relative wages attract immigrants and increase the labor force. In addition, there is the drift term and white noise. Equation (2.4) formalizes the movement of labor demand. Higher relative wages decrease labor demand. The drift term incorporates long-term trends owing to regional characteristics that can have both an increasing and a decreasing effect. In addition, there is white noise. Although our approach differs from the Blanchard and Katz model, their reasoning is present in the economic background of our models.

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It is not our objective to directly discuss the relationship between GDP, real wage adjustments, and unemployment. Instead, our main objective is simpler: Our study discusses how factor mobility adjusts as a direct response to different macroeconomic variables. Unemployment rates in the EA show significant variations. Rates in southern member countries are persistently higher than in core and northern member countries. Greece and Spain, for example, have shown double-digit rates from 2012 to 2021, although these were falling considerably from about a quarter to just below 15%. In Italy, the unemployment rate has fallen to just below 10%, whereas France managed a decline from about 10 to 8%. In the Netherlands, Austria, and Germany, rates were between 4 and 6%. The decline of unemployment rates in southern EA countries is related to an increase in their national population aged 15–64 working abroad in another EU or EFTA (European Free Trade Association) country (see Fig. 2.5). South-North labor migration, however, is not new in the EU. But compared to previous flows after the Second World War and in light of worsening southern labor markets, it is relatively low (see Lafleur & Stanek, 2018). In addition, with the exception of Portugal and Luxembourg, only Eastern European countries show migration rates above 8%. The recent East–West movement is much larger in both relative and absolute terms than the South-North counterpart (Lafleur & Stanek, 2018). Nominal wages increased moderately, by 18%, in the EA during the last nine years (2013–2021). In real terms the raise was of only 7%. Among the four major member countries, Germany’s pay increase was highest with 22 and 13% in nominal and real terms, respectively. In France and Spain, there still were real raises of about 5.5%. In Italy, however, real wages declined by almost 2%. Figures 2.6 and 2.7 show the development of nominal and real wages in the EA’s four major economies. Wage convergence cannot be detected despite the EU common market. Relatively high wage increases in Eastern EU countries—EA members and non-members alike—however are an expression of beta convergence.2 In three eastern countries, nominal wages tripled, while in five countries the nominal increase was still above 40%. In real terms, six eastern countries showed an increase beyond 40%. 2 For further discussion on beta convergence in light of the EU and EA, see Crespo Cuaresma et al. (2008) and Dreyer and Schmid (2017).

2013

2014 France

2015 Italy

2016

Spain

2017

2018

Greece

2019

2020

2021

Fig. 2.5 Ratio of nationals (15–64 years) working abroad in other EU/EFTA countries (Source ESTAT)

0.00% 2012

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

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2010 Germany

2012 France

2014

Italy

2016

Spain

2018

2020

Fig. 2.6 Nominal wages (industry, construction, services, no public admin, defense, social security) in the four largest EA economies (Source ESTAT)

14. 2008

16.

18.

20.

22.

24.

26.

28.

30.

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2013

2014 Germany

2015 France

2016

2017

Italy

2018

Spain

2019

2020

2021

Fig. 2.7 Real wages (industry, construction, services, no public admin, defense, social security) in the four largest EA economies (Source ESTAT, own calculations)

15 2012

17

19

21

23

25

27

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22 J. KABDERIAN DREYER AND P. A. SCHMID

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Summarizing, we suspect empirical responsiveness of wages and labor force participation to unemployment. Migration might be related to wages and unemployment.

2.4 Research Questions: Labor Mobility in Europe The literature on labor mobility at larger integrated markets—especially those with a common currency—is based on the OCA theory.3 This theory demands unrestricted labor mobility and thus responses of regional labor supply to asymmetric shocks. In the following, we summarize and categorize major work in the field and, afterward, establish our own research questions. Usually, regression and vector autoregressive models (VARs) are used in the literature to study labor mobility. Whereas regression models rest on one equation with a dependent and one or more independent variables, VAR models are used to estimate more than one equation simultaneously. The model represented by Eqs. (2.1) to (2.4), for example, would be implemented by a VAR. Even before the start of the Euro, researchers asked about the readiness of labor mobility as an adjustment mechanism in the later EA. Decressin and Fatás (1995) compared labor market dynamics in the first eleven EU countries and the United States for the 25 years up to the mid1990s. For the European countries, they identified 51 regions and for the United States they used all states. By regressing changes of employment in regions (states) against aggregate changes they found that employment changes were shared to a much larger extent in the United States in comparison with the EU. In addition, the authors investigated responses to regional shocks based on the Blanchard and Katz (BK) model from 1992. Applying several regressions, they showed the shocks were mostly absorbed by short-term regional adjustments of the labor force participation rates in the EU, whereas in the United States migration is affected immediately. Unemployment rates did not, however, play a large role in migration in both the EU and the United States. This led to persistent regional natural unemployment rates.

3 For a summary of the literature on labor mobility, please refer to the tables in Appendix 1.

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Later, Obstfeld and Peri (1998) analyzed the adjustment mechanisms on idiosyncratic macroeconomic shocks in the United States, Canada, and European countries (Germany, Italy, and the UK) using a broad approach. They accounted for labor mobility and interregional transfer payments. The authors worked with data from 1968 to 1995, applying descriptive statistics, simple regressions, and an application of the Blanchard and Katz (1992) model. The results indicated that interregional transfers played a much larger role outside the United States, especially in the European countries. One year after the start of the Euro, Puhani (2001) asked whether net immigration could adjust labor supply to accommodate asymmetric regional shocks. He investigated the labor markets of the EA’s core member countries (Germany,4 France, and Italy) and estimated log-linear multiple regression equations with panel data from the 1980s to the mid1990s. Gross regional population growth was explained by differences in regional and national unemployment rates as well as regional and national income (GDP) differences. Puhani (2001) could only identify responsiveness to unemployment differences for all three countries. Income differences, however, are not associated with gross population growth. Responses are different for the three countries: highest in Germany and lowest in Italy. Even for Germany, national migration was not sufficient to accommodate interregional unemployment differences. Even the high East–West migration after the German reunification could not adjust regional unemployment rates in the short run. Based thereon, Puhani (2001) estimated the same insufficient labor migration at the EA level, which was limited by non-existent common languages and a lack of coordinated social security systems. In the same year, Bentivogli and Pagano (1999) offered a comparably grim outlook. They used a two-stage least-squares (2SLS) linear regression model for the EA’s first eleven member countries and the years 1981 to 1994. The eleven countries are divided into 44 regions whose net migration in relation to population was explained by the logarithms of regional unemployment as well as income (real GDP) and the time variance of income that serves the role of uncertainty. Moreover, a set of control variables were used. In contrast to the United States, they

4 The panel data set included only data of the former Western Germany.

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could not identify a significant response of net migration to unemployment across the 44 European regions. Income, however, was found to be significant on both sides of the Atlantic. Besides, common language and federal tax and social security systems play a role in defining labor migration. Both studies were limited by old data samples and cannot account for effects of the integrated common market (EU) as well as the common currency (EA) on migration. The endogeneity hypothesis could not be tested. Puiu (2011) was one of the first whose research supported this early research. By presenting stylized facts from 2010 and EA-17 countries, he concluded that labor mobility was not sufficient in these seventeen countries to serve the role as an adjustment mechanism to asymmetric regional shocks. But there is more promising research, too. According to L’Angevin (2007), labor mobility between the United States and between member countries of the European Union started to converge following the run-up to the Euro’s introduction. L’Angevin analyzed data for the first 12 EA members from 1973 to 2005. Building on the Blanchard and Katz (1992) model, and accounting only for the years 1990 to 2005, L’Angevin found evidence that labor market reactions to labor demand shocks in the EU became more similar to those in the United States. The author concludes that labor mobility became a more efficient adjustment mechanism in the EA countries. In this context, it must be noted that Dao et al. (2014) identified important changes for labor mobility in the United States. They also used the Blanchard and Katz (1992) model and extended their research with 20 additional years. Based thereon, the role of the participation rate and unemployment increased in the United States for the absorption of labor demand shocks. Interestingly, during aggregate downturns, especially during the Great Recession from late 2007 to 2009, interstate migration played a larger role. This was counterintuitive as one might suspect that high mortgage debt after the bust of a housing boom decreased individuals’ interstate mobility (inter alia Kocherlakota, 2010). Beyer and Smets (2015), in addition, remade the comparison between the United States and Europe pursued earlier by Decressin and Fatás but with the addition of 20 years of data. The authors find a great difference in the response of mobility to region-specific shocks between the United States and Europe. They used data for the years 1977 to 2013 and relied on the BK model, which they estimated for both 51 United States and 48 EU regions. According to their research, responsiveness to regional

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labor demand shocks exists in both the EU and the United States. Labor mobility plays a larger role in the United States, where the adjustment was approximately twice as fast as in the EU. Dividing the total period into two subperiods (1977–1999 and 1990–2013), Beyer and Smets (2015) could show, however, convergence in the responsiveness of labor migration to labor demand shocks in the EU and the United States. This is due to a fall in interstate migration in the United States and increasing migration across the EU—a potential evidence in favor of the endogeneity hypothesis. Arpaia et al. (2016) could even show that migration doubled since the introduction of the Euro, supporting the endogeneity hypothesis. Estimating for 26 EU countries (except Romania and Bulgaria) and for the period 1970 to 2013, they showed that labor mobility absorbed a quarter of asymmetric shocks within one year. They applied the BK model, but used cross-country and not cross-regional migration. Long-standing EU15 countries showed labor mobility that is comparable with the United States. Among others, the reason for the cross-country perspective was the intention to analyze adjustments to country-specific shocks. We also follow this perspective in this book. Nevertheless, labor mobility across the EU was still not of the same magnitude as across the United States. According to an online survey among European labor market experts conducted by Krause et al. (2017), labor mobility was not achieved until 2014. Main obstacles identified were language barriers, a lack of recognition of professional qualifications, and non-harmonized social security systems. The results of Huart and Tchakpalla (2019) can be seen in this light. They used the data of 14 EA countries for the period 1999 to 2015. The net migration per 1,000 in destination countries was regressed against lagged net migration, the lagged difference in unemployment in the destination and the entire group of countries, the lagged difference in real wage growth rates, and the lagged difference in GDP per capita growth rates. Moreover, dummies like membership in the Schengen Area and free access to the national labor market for new member countries (from the Eastern Enlargement in 2004 and 2007) were used. For net migration, three different definitions were used. Besides net inflows from other EA countries, net inflows from other EU countries and from the rest of the world were considered. They found responsiveness of net migration to relative unemployment but not to wage and GDP per capita growth rates. Membership in Schengen is statistically not relevant for net migration, but

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access restrictions to national labor markets play a role in migration from the EU and the rest of the world. Overall, the responsiveness of migration to unemployment differentials was found to be low. Basso et al. (2019) compared labor mobility during the years 2007 to 2016 in 18 EA countries and the United States. Splitting the workingage population into native and foreign-born individuals, they showed that mobility is comparable in both panels for the foreign-born population, but not for the native population. Consequently, labor mobility could be improved for the native population in the EA. The major shortcomings of the European labor markets and obstacles to mobility are described by Dorn and Zweimüller (2021). They concluded that a single European labor market did not exist, on account of linguistic differences, non-coordinated regulations and, not to forget, discrimination against immigrants. Another reason for insufficient labor migration could be high homeownership rates and negative equity in the case of asymmetric demand shocks. This effect was verified by Modestino and Dennett (2018) in the United States for the time of the financial crises (2006–2009). High homeownership rates in peripheral EA countries (e.g., rates for Italy, Portugal & Spain more than 20% higher than for Germany) could thus be another reason for low responsiveness of European labor migration. Summarizing, the literature finds responsiveness of regional labor supply to asymmetric shocks, allowing labor mobility to serve its role as automatic stabilizer, both in the EA and the United States. In addition, the adjustment process in the EA improved in recent years and came closer to its American counterpart. Based on a recent dataset starting with the introduction of the Euro in 1998 until 2019 for all EU countries except Croatia, we try to answer similar research questions. The first three questions are analyzed by descriptive statistics: 1. Have migration flows increased over time in EU member countries so far in this century? In this case, the annual average of immigration and emigration flows are considered, both for intra-EU and total flows. Moreover, it is interesting to investigate any patterns of migration during the financial and sovereign debt crisis. 2. Has the standard deviation of migration flows increased over time in the EU? Again, we account for annual averages and ask about possible effects of the financial and sovereign debt crisis on

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labor mobility. A higher standard deviation might indicate more dynamism in EU migration flows. 3. Do migration volumes correlate with the standard deviation of GDP growth and unemployment? We take intra-EU average annual migration flows and look for visual evidence of co-movements. We analyze the fourth research question with a set of regression estimations. We follow Huart and Tchakpalla (2019) and take single countries as the unit of analysis: 4. How do migration flows respond to asymmetric shocks in the EU? We investigate both bilateral and total migration flows. Bilateral flows are those between two countries; total flows do not distinguish between single countries of origin. As independent variables, we consider—in line with the literature—relative unemployment, relative GDP per capita, relative GDP per capita growth, and relative social spending. Moreover, the distance between countries and several dummies—e.g., membership in the EU, the Schengen Area, or the EA—are accounted for.

2.5

The EU Capital Markets

The EU common market generally includes capital mobility across EU member countries, known as the freedom of movement of capital. Within the EU, individuals and entities have unrestricted access to financial services across borders in all member states. According to Article 63 TFEU, “all restrictions on the movement of capital” and “all restrictions on payments between member states and between member states and third countries shall be prohibited.” This does not, however, exclude differing national tax laws and financial regulations. Measures “to prevent infringements of national law and regulations, in particular in the field of taxation and the prudential supervision of financial institutions,” are not affected by the free movement of capital clause. Capital gains and dividends, for example, can be taxed differently in each member country. Thus, such provisions can still be used to channel capital flows. In addition, the legal framework that facilitates free movement of capital does not automatically lead to economic capital mobility. Consequently, the EU launched the Capital Markets Union initiative back in

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2015 with the aim of creating a single capital market. At the time of this writing, EU capital markets, in plural, are still fragmented. Besides regulatory and institutional fragmentation, the overreliance on bank financing and home bias is also seen as an obstacle (Camarero et al., 2021, p. 868). In the EA, the importance of the banking sector—measured as the ratio of its assets and GDP—is approximately three times the size of its US counterpart. The relative weight of financing provided by security markets in the United States is much higher. There, it plays the dominant role, whereas in the EA, financing is dominated by banks that channel funds from savers to borrowers. Recent literature has emphasized that bank-oriented financing systems are related to lower economic growth and amplified adverse shocks in comparison with market-oriented systems. However, this relationship could be biased by (shadow) banking crises (Breitenfellner & Schubert, 2017, p. 2). Maybe diversification of financing channels should be increased, as it is worse to rely only on one channel to finance the real economy. The diversification argument holds for savers in aging societies and for banks engaged in holding domestic sovereign securities, too. The home bias shall be overcome by lower transaction costs and greater transparency. Empirically, capital mobility increased across EA members in the long run. Even before the introduction of the Euro, the European Union experienced a deeper financial integration through the abolition of capital controls and the harmonization of financial regulations (Lane, 2013). In addition, the Euro removed the currency risk for member countries. As a result, obstacles to capital mobility decreased considerably within the EA in comparison with other advanced economies (Lane, 2009; Lane & Milesi-Ferretti, 2008). Camarero et al., (2021, p. 885) find that “the degree of capital mobility has been increasing during the 1990s after the Maastricht Treaty was signed.” Moreover, “European economic integration encouraged capital flows from the core countries to the periphery” (Camarero et al., 2021, p. 885). The financial and sovereign debt crises from 2007 to 2015, however, decreased capital mobility in the periphery. This had, however, no impact on the capital mobility of core countries (Camarero et al., 2021, p. 885). After the crises, “capital mobility shows a signal of recovery for the majority of Euroarea countries” (Camarero et al., 2021, p. 886).

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2.6 Adverse Shocks and Adjustments in Capital Markets As early as 1959, Ingram (1959) argued that capital mobility is an important adjustment mechanism in the event of asymmetric shocks in currency unions. In a world with perfect capital mobility, domestic investment does not depend on domestic savings (Stirböck & Heinemann, 1999). Thus, asymmetric shocks on domestic savings should not matter for domestic investments. In such cases, the capital inflow from foreign savings would counterbalance the lack of domestic ones. This mechanism should avoid adverse reactions of economic activity due to asymmetric shocks, with capital flowing to greater business opportunities in the affected countries. In Fig. 2.8, that follows the same logic of Fig. 2.2 but applied for capital mobility, one can see the argument: In the case of an economic shock in region B, national savings decrease owing to lower current income and worsening future expectations. The capital supply shortage increases the national interest rate in region B. This attracts foreign capital from region A as their savers are faced with a relatively lower national interest rate and see the higher rate in region B as an opportunity. This results in capital flows from A to B. These flows

Fig. 2.8 Capital mobility and adjustment rates (Source Krugman & Obstfeld, 2009, p. 156)

to

differing

interest

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continue until the interregional equilibrium rate i e has been reached in both regions. This way, capital mobility serves the role of an automatic stabilizer for the integrated economy of A and B. Generally, economists argue that EA “capital markets should be as integrated and developed as possible” (Camarero et al., 2021, p. 868). Lacking a sizable EA fiscal budget (see Dreyer & Schmid, 2015), the EA depends on the adjustments delivered by private capital markets. Integrated EA capital markets accelerate and increase the effectiveness of the European Central Bank (ECB)’s common monetary policy (Breitenfellner & Schubert, 2017, p. 3). In other words: “First, if private risk-sharing is grossly insufficient, it can limit the resilience of the Eurozone member states, measured as the capacity to absorb and recover from adverse shocks. Second, capital mobility may also strengthen the effectiveness of the single monetary policy as fragmentation and frictions may prevent the pass-through of the policy interest rates” (Camarero et al., 2021, p. 868). According to Verón and Wolff (2016, p. 137), “a substantial body of literature provides evidence that well-integrated and deep capital markets can spread country and region-specific risk, thereby smoothing the impact of strong recessions on consumption and investment.” This view is challenged by the literature, although one can relate enduring crises to a lack of international risk sharing, whose responsibility is seen in the reliance on a bank-oriented system. Thus, the dominant role of banks is seen as an obstacle to individual financing. Hybrid capital markets are seen as a solution. In such systems, banks transfer their risks by selling structured securities on capital markets. The major shortcoming is based on the idea that individual risk sharing can reduce aggregate uncertainty. Arrow’s impossibility theorem—self-fulfilling prophecies in the case of individual actions based on expectations and a lack of experience for conventional wisdom—stands against the holy grail of individual risk sharing as a solution for the economic impact of aggregate uncertainty. There is substantial literature that ascribes increased financial instability to international risk sharing (inter alia Bone & Suckling, 2004; Gabrisch, 2016). In other words, despite the spectacular boom of capital flows in the early years of the Euro, capital mobility proved to act in a destabilizing way during the financial crisis. Moreover, capital flows generated neither real income nor productivity convergence, according to Franks et al. (2018). The EA experienced a boom-bust cycle that was larger than

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similar cycles observed in the broader European region and in the global set of advanced economies (Lane, 2013). Thus, based on the literature, it is argued that capital mobility could, in theory, play the role of an automatic stabilizer of asymmetric shocks. However, financial stability could be affected negatively by larger capital mobility.

2.7 Research Questions: Capital Mobility in Europe Just as with labor mobility, according to OCA theory capital mobility must be unrestricted. This allows capital flows to respond to asymmetric shocks. In the following, we summarize and categorize major work in the field and, afterward, establish our research questions.5 Kondeas (2014) describes the role of capital mobility in the creation of asset bubbles and financial crises. He concludes that capital mobility can cause such bubbles and financial crises in “small or developing economies (…) when the capital flows are eventually reversed” (see Kondeas, 2014, p. 24). Kondeas (2014) bases his research on the two antagonistic views of the benefits and risks of capital mobility: the possibility of allocating capital without frictions to its best use, on the one hand, and the risk of the procyclicality of capital flows, on the other. He shows that for the Latin American crisis of the 1980s, the East Asian crisis of the 1990s and, especially, the EA crisis of the 2010s, less-developed countries lose in case of free capital mobility with liquidity reversals and crashing asset markets. In the EA, for example, free capital mobility and the eradication of the exchange rate risk reduced interest rates in member countries toward the lower German level. These lower levels were not used, however, by some member countries “to build productive capacity (…) [but] the cheaper capital was used to fuel consumption and unsustainable bubbles in securities and real estate markets” (Kondeas, 2014, p. 28). The temporary “economic prosperity was just an illusion” (Kondeas, 2014, p. 28). Drakos et al. (2018) investigate the relationship between national savings and investments for 14 EU member countries for the years

5 For a summary of the literature on capital mobility, please refer to the tables in Appendix 2.

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1970 to 2015. Based on the maximum-likelihood panel cointegration method, they find a long-run relationship between both variables. Although the savings retention coefficient was low, it was statistically significant, providing evidence for the Feldstein–Horioka puzzle. With free capital markets and capital mobility, there should be no correlation between national savings and investment, as savings search for their highest marginal product. Feldstein and Horioka (1980) first documented that the correlation between the national variables was not as one would expect with free capital mobility. Canale et al. (2018) investigate whether there is a trilemma between capital mobility, financial stability, and fiscal policy flexibility in the EA. Lacking a lender of last resort, the disciplining effects of financial markets on fiscal budgets might be increased in monetary unions. Based on the quarterly data of eleven EA countries from 1999 to 2012, Canale et al. (2018) show this trilemma and, as a consequence, argue for financial supervision in the EA. They construct three indices for the variables capital mobility, financial stability, and fiscal flexibility. Financial assets and liabilities include direct investments, portfolio investments, and other investments and reserves. Canale et al. (2018) first describe that an increase in one index is related to a decrease in at least one other index. Furthermore, they estimated an equation where the weighted sum of all indices adds up to one. As all coefficients were estimated to be positive and goodness of fit (R2 ) was high across all countries, the authors concluded that the trilemma did indeed exist. Globan (2014) focused on another trinity in the context of free capital mobility: independent monetary policy, a stable exchange rate, and financial openness. He showed for eight post-transition economies that the degree of capital mobility can be estimated by the reaction intensity to interest rate shocks. Owing to increased risk aversion, capital flows became less sensitive to interest rates in the aftermath of the 2007 financial crisis. Abiad et al. (2009) showed for a sample of 23 EU member countries and data from 1975 to 2004 (five-year, non-overlapping observations for each country) that—in contrast to recent literature—the EU sees the so-called downhill capital flows from richer to poorer countries bringing forward two advantages. First, rich countries build savings in countries that offer higher marginal products (owing to their lower capital-labor ratio) and poorer countries are able to catch up faster. They regress the current account balance to GDP ratio on variables like GDP per capita, lagged GDP growth per capita and the fiscal balance (as a ratio of GDP),

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and lagged foreign assets to GDP ratio. Second, financial integration and the interaction between financial integration and per capita income are included as explanatory variables. As the interaction term was observed to have a positive regression coefficient, financial integration facilitated downhill capital flows from richer to poorer member countries. Based thereon, we formulate our research questions. The first three, again, are answered by the presentation of stylized facts on bilateral data for pairs of countries, based on a recent dataset from 1998 to 2019: 1. How have capital flows evolved over time in the EU member countries? Moreover, which effect did the financial crisis have on capital flows? Owing to the hypothesis of downhill capital flows and increased risk aversion on international capital markets, a reversal of capital flows is expected with the end of the boom-bust cycle facilitated by low interest rates. 2. How has the variation in capital flows as a percentage of GDP across countries evolved? With increasing financial integration, we expect increasing variation, as investors can take opportunities in the case of asymmetric shocks that offer higher marginal products of capital abroad. 3. Do volumes of capital flows correlate with GDP growth volatility and unemployment? Would shock absorption by capital flows correlate with GDP growth volatility and unemployment? The fourth research question is addressed by the estimation of regression models: How do capital flows respond to asymmetric shocks? The independent variable is the change in the balance of total liabilities and claims, which is regressed on relative unemployment, relative GDP, and relative social spending.

References Abiad, A., Leigh, D., & Mody, A. (2009, April). Financial integration, capital mobility, and income convergence. Economic Policy, 24(58), 241–305. Arpaia, A., Kiss, A., Palvolgyi, B., & Turrini, A. (2016). Labour mobility and labour market adjustment in the EU. IZA Journal of Migration, 5, Article 21.

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Basso, G., D’Amuri, F., & Peri, G. (2019). Immigrants, labor market dynamics and adjustment to shocks in the Euro Area. IMF Economic Review, 67 , 528– 572. Bentivogli, C., & Pagano, P. (1999). Regional disparities and labour mobility: The Euro-11 versus the USA. Labour, 13(3), 737–760. Beyer, R. C. M., & Smets, F. (2015). Labour market adjustments and migration in Europe and the United States: How different? Economic Policy, 30(84), 643–682. Blanchard, O. J., & Katz, L. F. (1992). Regional evolutions. Brookings Papers on Economic Activity, 1992(1), 1–75. Bone, J. J., & Suckling, J. (2004). A simple risk-sharing experiment. Journal of Risk and Uncertainty, 28(1), 23–38. Breitenfellner , A., & Schubert, H. (2017). Potential und Risiken der Kapitalmarktunion für die Wirtschaft Europas und Österreichs [Potential and risks of the capital market union for the economy of Europe and Austria]. Research Centre International Economics Viena—FIW Policy Brief No. 35. Camarero, M., Muñoz, A., & Tamarit, C. (2021). 50 years of capital mobility in the Eurozone: Breaking the Feldstein-Horioka Puzzle. Open Economic Review, 32, 867–905. Canale, R. R., De Grauwe, P., Foresti, P., & Napolitano, O. (2018). Is there a trade-off between free capital mobility, financial stability and fiscal policy flexibility in the EMU? Review of World Economics, 154, 177–201. Crespo Cuaresma, J., Ritzberger-Grünwald, D., & Silgoner, M. A. (2008). Growth, convergence and EU membership. Applied Economics, 40(5), 643– 656. Dao, M., Furceri, D., & Loungani, P. (2014). Regional labor market adjustments in the United States. (IMF Working Paper Series WP/14/211). Decressin, J., & Fatás, A. (1995). Regional labor market dynamics in Europe. European Economic Review, 39(9), 1627–1655. Dorn, D., & Zweimüller, J. (2021). Migration and labor market integration in Europe. Journal of Economic Perspectives, 35(2), 49–76. Dreyer, J. K., & Graversen, M. B. (2014). An incomplete optimal currency area: The issue of migration in the Eurozone. Review of Business Research, 14(2), 61–72. Dreyer, J. K., & Schmid, P. A. (2015). Fiscal federalism in monetary unions: Hypothetical fiscal transfers within the Euro-zone. International Review of Applied Economics, 29(4), 506–532. Dreyer, J. K., & Schmid, P. A. (2017). Growth effects of EU and EZ memberships: Empirical findings from the first 15 years of the Euro. Economic Modelling, 67 , 45–54. Dreyer, J. K., & Schmid, P. A. (2020). Optimal currency areas and the euro (Vol. I): Business cycles synchronization. Springer Nature.

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Dreyer, J. K., Wolffsen, P., & Mortensen, M. (2014). Wealth and immigration in Denmark: An analysis of human capital. Journal of Academy of Business and Economics, 14(2), 19–36. Drakos, A. A., Kouretas, G. P., & Vlamis, P. (2018). Saving, investment and capital mobility in EU member countries: A panel data analysis of the Feldstein-Horioka puzzle. Applied Economics, 50(34–35), 3798–3811. Fahri, I., & Werning, I. (2015). Labor mobility within currency unions. (NBER Working Paper Series No. 20105). Feldstein, M., & Horioka, C. (1980). Domestic saving and international capital flows. The Economic Journal, 90, 314–329. Franks, J. R., Barkbu, B. B., Blavy, R., Oman, W., & Schoelermann, H. (2018). Economic convergence in the euro area: Coming together or drifting apart? (IMF Working Paper No. 18/10). Globan, T. (2014). Testing the ‘trilemma’ in post-transition Europe – a new empirical measure of capital mobility. Post-Communist Economies, 26(4), 459– 476. Gabrisch, H. (2016). Zur kritik der kapitalmarktunion [On the criticism of the capital markets union]. Wirtschaftsdienst, 96(12), 891–899. House, C. L., Proebsting, C., & Tesar, L. L. (2018). Quantifying the benefits of labor mobility in a currency union. (NBER Working Paper Series No. 25347). Huart, F., & Tchakpalla, M. (2019). Labor market conditions and geographic mobility in the Eurozone. Comparative Economic Studies, 61, 263–284. Ingram, J. (1959). State and regional payments mechanisms. Quarterly Journal of Economics, 73(4), 619–632. Kocherlakota, N. (2010). Inside the FOMC. President’s Speech, Marquette, MI. August 17, 2010. Kondeas, A. G. (2014). Capital mobility and financial crises. International Journal of Finance and Policy Analysis, 6(1–2), 24–35. Krause, A., Rinne, U., & Zimmermann, K. F. (2017). European labor market integration: What the experts think. International Journal of Manpower, 38(7), 954–974. Krugman, P., & Obstfeld, M. (2009). International economics: Theory and policy (Global Edition). Pearson Addison-Wesley. Lafleur, J.-M., & Stanek, M. (2018). Southern European migration towards Northern Europe. In IEMed. Mediterranean Yearbook 2018 (pp. 325–328). European Institute of the Mediterranean. Lane, P. R. (2009). EMU and financial integration. In B. Mackowiak, F. Mongelli, G. Noblet, & F. Smets (Eds.), The euro at ten: Lessons and challenges (pp. 82–115). European Central Bank. Lane, P. (2013). Capital flows in the euro area. (European Commission Directorate-General for Economic and Financial Affairs Economic Papers No. 497).

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Lane, P. R., & Milesi-Ferretti, G. M. (2008). The drivers of financial globalization. The American Economic Review, 98(2), 327–332. L’Angevin, C. (2007). Labour market adjustment dynamics and labour mobility within the euro area. (Les Documents de travail de la DGTPE Numéro 2007/ 06.) [DG Trésor Working Document No. 2008/02]. Modestino, A. S., & Dennett (2018). Are American homeowners locked into their houses? The impact of housing market conditions on state-to-state migration. (Federal Reserve Bank of Boston Working Papers, No. 12–1). Mongelli, F. P. (2008). European economic and monetary integration, and the optimum currency area theory, Directorate General Economic and Monetary Affairs (DG ECFIN), No. 302, European Commission. Obstfeld, M., & Peri, G. (1998). Regional non-adjustment and fiscal policy. Economic Policy, 13(26), 205–259. Puhani, P. (2001). Labour mobility: An adjustment mechanism in Euroland? Empirical evidence for Western Germany. France and Italy. German Economic Review, 2(2), 127–140. Puiu (2011). Labour mobility as an adjustment mechanism in the euro area. CES Working Papers, 3(4), 579–591. Véron, N., & Wolff, G. B. (2016). Capital markets union: A vision for the long term. Journal of Financial Regulation, 2(1), 130–153. Stirböck, C., & Heinemann, F. (1999). Capital mobility within EMU . (ZEW Discussion Papers, No. 99–19).

CHAPTER 3

Labor Mobility, the Empirics

Abstract In this chapter, we conduct our quantitative analysis on labor mobility. Given data limitation, some of our research questions are analyzed using descriptive statistics and graphical analysis, while others make use of panel regression models. The chapter points to an increase of intra-EU migration over the sample period, suggesting an endogenous development of the region toward an OCA over time. Migration flows react to unemployment in the EA as well as work as an indirect redistribution tool from richer to poorer countries. EA membership even intensifies EU intra-migration. Keywords Optimal Currency Areas · Determinants of migration · Economic stabilization · EA migration · Endogenous development

3.1

Labor Mobility: Methodology of Analysis

To study labor mobility, we follow with the investigation of our four research questions posed in Sect. 2.4. We use descriptive graphical analysis to answer the first three questions and regressions for the investigation of the fourth. However, to answer question 4, we divide our analysis into different variables of interest. We start by assuming that migrants look at © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kabderian Dreyer and P. A. Schmid, Optimal Currency Areas and the Euro, Volume II, https://doi.org/10.1007/978-3-031-38867-5_3

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other European countries as a union, comparing their country’s economic situation to that of the others as a group. A decision to migrate is taken when neighbors are better off and offer better economic possibilities. In this case, we regress the following model: N et Mig i,t u i,t G D P i,t = β0 + β1 + β2 popi,t u EU,t G D P EU,t SocSpei,t /G D P i,t + εi,t + β3 SocSpe EU,t /G D P EU,t

(3.1)

where N et Mig i,t is the difference between immigrants and emigrants in country i in year t, popi,t is the population of country i in year t, u i,t is the unemployment rate in country i in year t, u EU,t is the average EU unemployment rate in year t, G D P i,t is the per capita GDP of country i in USD in year t, G D P EU,t is the average per capita GDP of EU countries in USD in year t, SocSpei,t is the per capita social spending of country i in USD in year t, and Social Spend EU,t is the average per capita social spending of the different EU countries in USD in year t. The main objective of this first regression exercise is to check whether net migration negatively correlates with relative unemployment so that it acts as a tool for the members of the common currency, to smooth asymmetric shocks. Moreover, we also test whether migration can be motivated by the relative wealth in a country (measured by the ratio of per capita GDPs) or even by an opportunistic behavior, where the migrant would leave his/her country with the objective of benefiting from higher social subsidies in other countries (“misuse of welfare states”). To check for any influence of EU and EA memberships in migration decisions, as well as to test for a tendency toward increasing migration intensity over time, we also chose to regress what we call the total volume of migration of a country: the sum of its emigration and immigration flows. In this case, we regressed the following aggregate model: | | | | V ol Mig i,t = β0 + β1 |u i,t − u EU,t | + β2 |G D P i,t − G D P EU,t | popi,t | | | SocSpei,t SocSpe EU,t | | + β4 EU i,t + β5 E Ai,t | + β3 | − G D P i,t G D P EU,t | + β6 Y ear t + εi,t (3.2)

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41

where V ol Mig i,t is the sum of emigration and immigration numbers in country i at time t. The variables indicating relative unemployment, relative per capita GDP, and relative social spending are similar to those in Eq. (3.1), but are now presented in absolute differences instead, to take into account the positive characteristic of the dependent variable. EU i,t and E Ai,t are dummy variables that assume the values of 1 if a country is a member of the respective union, and 0 otherwise. The year counter is added to search for a yearly tendency in the intensity of migration flows. One could think about adding these dummies also in Eq. (3.1). This would make no sense, however, as net migration in Eq. (3.1) is the balance of immigration and emigration. Thus, although migration flows could be high owing to EA and EU memberships, net migration can be small. We expect that big differences in the three macro-independent variables lead to higher intensity of migration flows. Big differentials in unemployment, wealth, or social subsidies should increase migration intensity for the specific country. Moreover, if there is a differential of being a member of the EU or EA in terms of motivation for migration, the estimates associated with the dummy variables should be significant. Moving forward, it could also be interesting to investigate whether people can base their migration decision on bilateral comparisons, instead of only comparing his/her country to those averages of a union. In this case, we need to substitute our models with bilateral variables, thus: N et Mig i, j,t popi,t

u i,t G D P i,t + β2 u j,t G D P j,t SocSpei,t /G D P i,t + β3 + εi, j,t SocSpe j,t /G D P j,t = β0 + β1

(3.3)

where the subscript “i, j” refers to the bilateral relation between country i and country j. Thus, in this case, it is about comparing a variable in one country with another and not with the average of the union. The economic logic of the different variables in Eq. (3.3) follows those in Eq. (3.1), except for the fact that they appear as relative to other countries rather than to the union. We would expect, however, that they influence migration in a similar way. Finally, if we look at total volume of migration in bilateral terms, we could again test the importance of EU and EA membership as well as

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tendency in time, but for the bilateral setting. Moreover, since we have bilateral data, we could also include the variable distance between two countries, assuming that people might prefer to migrate to countries that are closer to their last residencies. In this case, we would achieve the fourth and final migration model: V ol Mig i, j,t popi,t

| | | | = β0 + β1 |u i,t − u j,t | + β2 |G D P i,t − G D P j,t | | | | SocSpei,t SocSpe j,t | | + β4 EU i, j,t + β5 E Ai, j,t + β3 || − G D P i,t G D P j,t | + β6 Y ear t + β7 Dist i, j + εi, j,t

(3.4)

where all economic variables in Eq. (3.4) follow the rationale of Eq. (3.2), except for their bilateral setting. Notice that given the bilateral setting of Eq. (3.4), the dummy variables EU and EA assume the value of one when both countries i and j are members of the respective union, and 0 otherwise. dist i, j is the distance in kilometers between the capitals of countries i and j. We would expect greater distances between two countries to decrease bilateral intentions of migration. In all our estimations we will be conservative, applying simple regressions first and then following with multiple regressions. Moreover, all models will be estimated using multiple regressions, applying all possible combinations of independent variables one by one. This will allow us to check robustness (change in estimate signs and significance) as well as possible model misspecification and multicollinearity between variables.1 Thus, besides simple regressions of our independent variables, we estimate four independent models—A, B, C, and D—according to the following combinations of relative independent variables of Table 3.1, using the ordinary least-squares (OLS) regression2 :

1 In our first pivot (Dreyer & Schmid, 2020), we have controlled for multicollinearity by conducting a variance inflation factor (VIF) analysis. In the case of this second pivot, this was not found necessary given the low number of independent variables. 2 Notice that endogeneity is a frequent limitation of this kind of estimation, and that

one could potentially treat part of this problem using the generalized method of moments, GMM (Hansen, 1982). However, we decided to keep it simple and follow the analysis using the panel OLS for two different reasons: (1) The use of the GMM alone does not guarantee that endogeneity issues will be corrected (Alizadeh et al., 2021); and (2) The problem of weak instruments often leads to weak identification (see Stock & Wright,

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LABOR MOBILITY, THE EMPIRICS

43

Table 3.1 Combination of relative independent variables for multiple regression estimates Model A

Model B

Unemployment Wealth (per capita GDP)

Unemployment

Social Spending

Model C

Model D

Wealth (per capita GDP) Social Spending

Unemployment Wealth (per capita GDP) Social Spending

We structure the chapter by starting with a few general descriptive considerations in relation to our data, followed by a more “precise” econometrical work when our data allows. For more details on the data collected, please refer to Appendix 3.

3.2

Descriptive Analysis

Do bilateral migration flows increase over time in the EU? Does the crisis affect these flows? To understand our data we decided to observe intraand total migration flows of EU countries over time. In each year, we took the average of these variables to construct each point in the graphs below, as well as calculate the ratio between intra- and total migration flows. The volume of flows is defined as the sum of immigration and emigration. Notice in Fig. 3.1 that migration flows grow in time in general. This phenomenon is not restricted to intra-EU migration: It increases in general, including non-EU migration, over time. However, in graph 3 of Fig. 3.1, one can notice that there is a tendency toward increases in participation of intra-EU migration relative to total migration over the 20 years of our sample period. We can observe that this participation almost doubled, which can be considered a good sign for the development of labor mobility in the EU. Despite all cultural differences, including difference in languages, migration between EU countries has been increasing rapidly during these years. This is true even though, originally, different economists were skeptical about migration adjustments in the EA. Concerning the Euro crisis around 2010, we can see in the

2000; Dreyer et al., 2013, 2020). Our independent variables are heavily autocorrelated with their own lags, which can lead to problems in the inversion of the GMM covariance matrix and therefore to unstable estimates.

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J. KABDERIAN DREYER AND P. A. SCHMID

different graphs of Fig. 3.1 that migration flows peaked at this time. This peaking characteristic can also be observed in the relation between intraand total EU migration. Although we cannot attest statistical significance, we can at least observe visually that the intensity of migration, and more specifically intra-EU migration, reached a peak around the crisis period. After observing these average migration flows, it could be interesting to look at their volatilities. Do migration flows become more dynamic in time in the Euro-Area so that they act as a response to asymmetric economic shocks? Fig. 3.2 gives us a snapshot of these volatilities over time. Figure 3.2 indicates a sharp increase in the intensity of dynamism in migration flows over the time covered by our sample period. Notice that the volatilities of both intra- and total EU migration increase over time. However, in the first two graphs of Fig. 3.2, one can clearly see that the volatilities of intra-EU migration increase much more than those of total migration. This is then confirmed in graph 3 of Fig. 3.2, with a sharp increase in the relative volatility between intra- and total EU migration. This can be an indication that during the sample period, migration becomes a more relevant tool of the common currency area in order to counteract economic shocks. Again, as a preliminary analysis, it could also be interesting to see how the volume of migration in the EU relates to the volatility of GDP growth and of unemployment. Figure 3.3 gives us a snapshot of these relations over time. It is hard to identify any patterns in Fig. 3.3. This goes in line with the findings of Huart and Tchakpalla (2019), who claim that although a relationship exists between migration and unemployment, the same type of correlation is not found when comparing migration to GDP growth. Likely, the relation between per capita GDP growth and migration is dynamic and does not happen in a single point in time. We first tried to use lags of GDP growth and unemployment to account for a possible one-time lag in the decision to migrate or not. This is illustrated in Fig. 3.4. Figure 3.4 looks very much like Fig. 3.3. Put simply, the low per capita GDP growth over time might influence migration in future points of time. Using a one-time lag for our plots, as shown in Fig. 3.4, is not enough to account for this problem. Thus, it would be interesting to analyze a dynamic regression model and study these relations. However, we face a limitation of data points for possible regression estimations, since the number of years in our sample equals 20.

Fig. 3.1 Intra- and total migration flows of EU countries over time

3 LABOR MOBILITY, THE EMPIRICS

45

Fig. 3.2 Volatilities of intra- and total EU migration and their relation over time

46 J. KABDERIAN DREYER AND P. A. SCHMID

Fig. 3.3 Per capita GDP growth and unemployment vs. volatility of intra-EU migration

3 LABOR MOBILITY, THE EMPIRICS

47

Fig. 3.4 Lag per capita GDP growth and lag unemployment vs. volatility of intra-EU migration

48 J. KABDERIAN DREYER AND P. A. SCHMID

3

3.3

LABOR MOBILITY, THE EMPIRICS

49

A Closer Look into Migration Flows

To have a better understanding of the relationship between intra-EU migration flows and different economic variables, we regress migration variables on them. Thus, we start with checking for an association between measures of migration with variables that indicate relative unemployment rates, relative social spending, and relative per capita GDP levels. 3.3.1

Net Migration Flows—Country vs. EU Region

In our first estimations, the term relative refers to the relationship (ratio) between independent variables in a country and their average in the entire EU region. These are used to explain net intra-EU migration rates in each country. We start with simple regressions and follow with multiple regressions, where these independent variables are combined to explain net migration flows. By combining simple with multiple regressions we can test our estimates for robustness as we will be able to observe whether results indicate the same coefficient signs independently of model design. Notice that we systematically run our estimations first with the entire dataset and afterward remove outliers following the 4/n Cook’s distance criterion on all our regression residuals, as for example in Dreyer et al. (2023). For more details on the Cook’s distance method, see Appendix 4. 3.3.1.1 Simple Regressions Table 3.2 reports simple regression estimations where net intra-EU migration flows relative to the population are explained by these variables. According to Table 3.2, after correction for outliers, all estimated coefficients associated with our independent variables are statistically significant (at least at the 5% level) and explain a significant part of the variance in net migration flows. Moreover, coefficients exhibit the following expected signs: • The higher the relative unemployment, the lower the net immigration observed in the country. This is in line with our expectation: When unemployment rates are relatively higher, people should leave the country and find jobs in other countries where the job market offers better conditions. This is what we should expect to observe in

0.0039870 (0.0014275)**

0.0024496 (0.0022892)

G D P i,t G D P EU,t

SocSpei,t /G D P i,t SocSpe EU,t /G D P EU,t

0.0167

0.3063

0.1064

R2

N et Mig i,t popi,t

0.0144

0.3046

0.1043

Adj. R2

0.0030134 (0.0004896)*** 0.0027129 (0.0012770)*

−0.0018164 (0.0007071)*

Estimate

Removing outliers

0.0696

0.3877

0.0869

R2

0.0673

0.3861

0.0846

Adj. R2

This table reports simple regression estimations of net migration flows in different European countries against the EU region. For simplification reasons, we only report the results of the inclination coefficient. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

−0.0031007 (0.0011044)**

Estimate

Full sample

Simple regressions—dependent variable:

u EU,t

u i,t

Ind. Var

Table 3.2

50 J. KABDERIAN DREYER AND P. A. SCHMID

3

LABOR MOBILITY, THE EMPIRICS

51

optimal currency areas, where migration plays an important role in smoothing asymmetric shocks. • The higher the GDP per capita in a country compared to the EU, the richer a country is compared to the region and therefore the more people are expected to immigrate to that country in search of better living conditions. This means that the common currency area also has an indirect and natural role on redistributing wealth. • The more a country spends in social benefits, the more people migrate to that country. So, migrants are in search of countries with higher welfare levels. 3.3.1.2 Multiple Regressions Table 3.3 reports multiple regression estimates where net flows of intraEU migration relative to the population are explained by all possible combinations of independent variables. Notice that no matter the combination of variables, the signs of our estimates when significant go in line with their estimated results in the simple regressions. However, some of them lose significance when combining variables. For example, the combination between social spending and relative GDP per capita seems to inflate the variance of the model, leading to lack of significance of the estimate related to social spending. The same type of issue occurs with the variable relative unemployment when used together with relative GDP per capita. Apparently, richer countries in the EU (those with higher relative GDP) are exactly those that have higher relative social spending. Likewise, richer countries in the EU are exactly those that had lowest relative unemployment over the sample period. Thus, combining these variables does not imply that one of them is less important when it comes to explaining net migration flows. It simply means that the variables to some extent say the same. All our multiple regression models indicate that: • Relative unemployment is inversely related to net immigration. So, migration responds to relative unemployment and smooths differences in economic states between countries of the common currency. • Net migration smooths differences in per capita GDPs, acting as an “indirect redistribution tool” for the common currency area. • Net migration also correlates with differences in social spending. Migrants search for those countries that provide higher levels of social spending or, in other words, those that provide higher levels of welfare to their populations.

0.3161 0.3128 −3510 −3494 97.5*** 422

0.0023750 (0.0021002) 0.1222 0.1180 −3404 −3388 29.3*** 422

-0.0023412 (0.0023142) 0.3189 0.3157 −3512 −3496 98.8*** 422

−0.0020049 (0.0023886) 0.3250 0.3202 −3514 −3493 67.5*** 421 0.3943 0.3912 −3746 −3730 127.3*** 391

0.0043600 0.0040356 0.0029791 (0.0016125)** (0.0017728)* (0.0005714)***

−0.0024573 (0.0009133)** −0.0002524 (0.0004446)

0.0029365 (0.0012386)* 0.1758 0.1717 −3651 −3635 41.9*** 393

−0.0022664 (0.0011272)* −0.0003124 (0.0004722)

D

−0.0000041 (0.0009609) 0.4029 0.3998 −3766 −3750 130.9*** 388

0.0000223 (0.0010434) 0.4176 0.4131 −3770 −3750 92.9*** 389

0.0029505 0.0028937 (0.0004666)*** (0.0004298)***

−0.0011866 −0.0026219 (0.0015847) (0.0012329)* −0.0017143 (0.0005151)***

C

This table reports multiple regression estimations of net migration flows in different European countries against the EU region. In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

R2 Adj. R2 AIC BIC F DF

0.0036495 (0.00015437)*

−0.0004673 (0.0024464) −0.0008354 (0.0008858)

0.0013623 −0.0012946 (0.0027316) (0.0023382) −0.0030862 (0.0011332)**

−0.0019413 (0.0020255) −0.0010411 (0.0008632)

B

A

D

B

A

C

Removing outliers

N et Mig i,t popi,t

Full sample

Multiple regressions—dependent variable:

SocSpei,t /G D P i,t SocSpe EU,t /G D P EU,t

G D P i,t G D P EU,t

u i,t u EU,t

Intercept

Table 3.3

52 J. KABDERIAN DREYER AND P. A. SCHMID

3

3.3.2

LABOR MOBILITY, THE EMPIRICS

53

Total Migration Flows—Country vs. EU Region

We also wanted to explore a few other factors that could potentially affect migration. For example, do EU and EA memberships influence migration decisions in the European setting? Is there a tendency over time for higher migration flows within countries of the European region? Unfortunately, we cannot answer these membership questions by regressing net migration flows, as they have to sum 0 in each period. Thus, to check for these relations, we regress the total volumes of migration on the same relative independent variables but expressed in absolute differences instead of ratios. In other words, does the total value (the sum) of emigration and immigration in a country depend on the absolute value of the differences between unemployment in that country and the average unemployment of the region, GDP per capita in that country and the average GDP per capita of the region, and social spending in that country and the average social spending of the region? 3.3.2.1 Simple Regressions Table 3.4 shows simple regression estimates where total intra-EU migration flows relative to the population are explained by the independent variables now expressed in absolute differences. Notice in this case that none of the estimates are statistically significant, although their signs make sense: Higher differences in unemployment between a country and the EU, in wealth (GDP per capita) and in social spending, should lead to higher migration volumes in a country. By summing emigration and immigration numbers as well as taking absolute values of differences in our independent variables, we might hide some economic effects that our regressions on net migration were able to catch. This, in turn, leads to very low R2s. Thus, the results of these simple regressions are of little use. 3.3.2.2 Multiple Regressions Our main objective with regressing total flows is to study the degree to which EU and EA memberships influence intra-EU migration levels as well as to account for a possible tendency in time of intra-EU migration flows. Thus, we continue our work by including variables that can control for such effects: one dummy variable for EU membership, one dummy variable for EA membership, and one year counter to account for a tendency toward migration flows over time.

0.0054

0.3301 0.0031

0.3285

−0.0017

Adj. R2

0.0000002 (0.0000001) 0.0001379 (0.0003024)

0.0001184 (0.0001534)

Estimate

Removing outliers

0.0031

0.0726

0.0039

R2

0.0006

0.0702

0.0014

Adj. R2

This table reports simple regression estimations of total migration flows in different European countries against the EU region. For simplification reasons, we only report the results of the inclination coefficient. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

| | SocSpe | SocSpei,t | − G D P EU,t | | GDP i,t EU,t

0.0006

0.0000004 (0.0000002) −0.0003233 (0.0007935)

R2

V ol Mig i,t popi,t

0.0000811 (0.0002444)

Estimate

Full sample

Simple regressions—dependent variable:

| | |u i,t − u EU,t | | | |G D P i,t − G D P EU,t |

Ind. Var

Table 3.4

54 J. KABDERIAN DREYER AND P. A. SCHMID

3

LABOR MOBILITY, THE EMPIRICS

55

Tables 3.5, 3.6, 3.7 and 3.8 report multiple regression estimates, where migration volumes relative to the population are explained by all possible combinations of our independent variables. However, even with the expected signs, estimates associated with the differences in unemployment and in social spending are not statistically significant. The difference in GDP per capita is the only one that can explain the model’s variance— not only exhibiting statistical significance, but also leading to significant increases in the model’s R2s. Otherwise, we observe the following interesting effects: • There is a statistically significant effect over time on total intra-EU migration flows. The longer the time in our sample, the more significant the migration flows. This confirms the results of our descriptive analysis. • Migrants do not necessarily care about moving within the EU, as the estimate of this dummy variable has no statistical significance. • However, migrants take into account Euro membership (EA) considerations when deciding whether to move. They prefer moving to countries that adopt the Euro as a currency. This effect is significant overall.

3.3.3

Net Migration Flows—Country vs. Country (Bilateral Relations)

We also wanted to investigate whether migration flows can be defined by bilateral references. Do specific macroeconomic differences between two countries motivate migration? We repeat our regressions using bilateral migration data. We start with net bilateral migration flows and move then to total volumes, where characteristics such as EU, EA, tendency over time, and the distance between two countries are used as control variables. 3.3.3.1 Simple Regressions Table 3.9 show simple regression estimates where net bilateral migration relative to the population of each country is explained by the same types of bilateral variables used in our prior estimations. However, instead of expressing variables relative to the average of other European countries, we use other countries as the base level. Therefore, we use the term “bilateral variables.”

| | |u i,t − u EU,t |

Intercept

0.3301 0.327 −2931 −2915 104.0*** 422

0.3862 0.3818 −2966 −2946 88.2*** 421

0.0004179 (0.0001452)**

0.0002214 (0.0001672) 0.0059704 0.0050215 (0.0033767) (0.0043859) 0.0060663 0.0056722 (0.0024065)* (0.0023282)* 0.4961 0.5102 0.4913 0.5044 −3048 −3058 −3024 −3030 103.4*** 87.3*** 420 419 0.0926 0.0878 −3005 −2989 19.6*** 385

0.1891 0.1829 −3107 −3087 30.3*** 390

0.0003294 (0.0000859)*** 0.0031018 (0.0023485) 0.0034185 (0.0015893)* 0.1769 0.1686 −3161 −3137 21.3*** 397

0.0002481 (0.0000911)** 0.0011788 (0.0024455) 0.0024828 (0.0013871) 0.2283 0.2185 −3192 −3164 23.3*** 395

−0.6570632 −0.0016422 −0.4970771 (0.1723882)*** (0.0024559) (0.1812614)** 0.0000566 0.0000327 0.0000047 (0.0001342) (0.0001207) (0.0001250) 0.0000001 0.0000003 0.0000002 (0.0000001) (0.0000001)*** (0.0000001)**

Removing outliers

V ol Mig i,t popi,t

This table reports multiple regression estimations of total migration flows in different European countries against the EU region. In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. This table reports estimations for Model A. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

R2 Adj. R2 AIC BIC F DF

E Ai,t

EU i,t

Y ear t

| | SocSpei,t | G D P i,t

| |G D P i,t

Full sample

A

Multiple regression Model A—dependent variable:

0.0021723 −0.8379123 −0.0085366 −0.4523427 0.0044449 (0.0032852) (0.2927449)** (0.0056087) (0.3320310) (0.0016113)** −0.0000180 −0.0000114 0.0000162 0.0000078 0.0001757 (0.0002221) (0.0001670) (0.0001465) (0.0001541) (0.0001238) | − G D P EU,t | 0.0000004 0.0000004 0.0000005 0.0000005 0.0000002 (0.0000002) (0.0000002) (0.0000002)** (0.0000002)** (0.0000001) | SocSpe EU,t | − G D P EU,t |

Table 3.5

56 J. KABDERIAN DREYER AND P. A. SCHMID

0.0101325 −0.9403701 (0.0045247)* (0.2838576)** 0.0000307 0.0000485 (0.0002430) (0.0002343)

Full sample

B Removing outliers

V ol Mig i,t popi,t

This table reports multiple regression estimations of total migration flows in different European countries against the EU region. In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. This table reports estimations for Model B. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

0.0002488 0.0001908 0.0003170 (0.0003433) (0.0003240) (0.0003905) 0.0003265 0.0003446 (0.0001049)** (0.0001249)** 0.0005983 −0.0017711 (0.0029306) (0.0030468) 0.0014746 0.0010741 (0.0015411) (0.0015385) 0.1114 0.0304 0.1263 0.1049 0.0208 0.1155 −3170 −3120 −3158 −3150 −3096 −3130 17.0*** 3.1* 11.7*** 407 401 406

0.0080941 −0.8696432 0.0067610 −0.6499118 0.0053982 −0.6852212 (0.0041765) (0.2587264)*** (0.0015450)*** (0.2115006)** (0.0031002) (0.2505160)** 0.0000073 0.0000008 0.0001106 0.0001208 0.0000700 0.0000918 (0.0002492) (0.0002476) (0.0001775) (0.0001659) (0.0001592) (0.0001708)

Multiple regression Model B—dependent variable:

| | |G D P i,t − G D P EU,t | | | SocSpe EU,t | | SocSpei,t −0.0002466 −0.0001622 0.0001937 | G D P i,t − G D P EU,t | −0.0003140 −0.0002317 (0.0008315) (0.0007730) (0.0008813) (0.0008332) (0.0003039) Y ear t 0.0004726 0.0004373 (0.0001421)*** (0.0001294)*** EU i,t −0.0001393 −0.0016787 (0.0037192) (0.0030572) E Ai,t 0.0036436 0.0030603 (0.0033029) (0.0025978) 2 R 0.0055 0.0771 0.0424 0.0990 0.0075 Adj. R2 0.0008 0.0757 0.0333 0.0883 0.0026 AIC −2763 −2793 −2775 −2799 −3127 BIC −2747 −2772 −2751 −2771 −3111 F 1.1 11.7*** 4.6** 9.2*** 1.5 DF 422 421 420 419 408

| | |u i,t − u EU,t |

Intercept

Table 3.6

3 LABOR MOBILITY, THE EMPIRICS

57

Full sample

C

Multiple regression Model C—dependent variable:

Removing outliers

V ol Mig i,t popi,t

This table reports multiple regression estimations of total migration flows in different European countries against the EU region. In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. This table reports estimations for Model C. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

Intercept

0.0018819 −0.8441258 −0.0091217 −0.4636029 0.0042958 −0.6834535 −0.0020379 −0.5299191 (0.0029823) (0.3109058)** (0.0054929) (0.3498709) (0.0019552)* (0.1748291)*** (0.0027044) (0.2086122)* | | |u i,t − u EU,t | | | |G D P i,t − G D P EU,t | 0.0000004 0.0000004 0.0000005 0.0000005 0.0000002 0.0000001 0.0000003 0.0000002 (0.0000002) (0.0000002) (0.0000002)** (0.0000002)** (0.0000001) (0.0000001) (0.0000001)** (0.0000001)** | | SocSpe EU,t | | SocSpei,t 0.0001933 0.0002238 0.0001763 0.0002846 0.0001972 0.0002852 | G D P i,t − G D P EU,t | 0.0000625 0.0001225 (0.0003861) (0.0003661) (0.0003958) (0.0003942) (0.0002640) (0.0002628) (0.0002997) (0.0003030) Y ear t 0.0004208 0.0002267 0.0003421 0.0002641 (0.0001541)** (0.0001758) (0.0000869)*** (0.0001045)* EU i,t 0.0057524 0.0047713 0.0028452 0.0007602 (0.0034778) (0.0046859) (0.0025515) (0.0026516) E Ai,t 0.0062269 0.0058396 0.0035107 0.0025308 (0.0023834)** (0.0022870)* (0.0015659)* (0.0013526) 2 R 0.3303 0.3869 0.4979 0.5127 0.0832 0.2092 0.1788 0.2387 Adj. R2 0.3271 0.3825 0.4932 0.5069 0.0785 0.2031 0.1706 0.2291 AIC −2931 −2967 −3049 −3060 −3039 −3116 −3169 −3189 BIC −2915 −2946 −3025 −3032 −3023 −3096 −3145 −3161 F 104.1*** 88.5*** 104.1*** 88.1*** 17.6*** 34.3*** 21.7*** 24.7*** DF 422 421 420 419 389 389 399 395

Table 3.7

58 J. KABDERIAN DREYER AND P. A. SCHMID

Full sample

D

Multiple regression Model D—dependent variable:

Removing outliers

V ol Mig i,t popi,t

This table reports multiple regression estimations of total migration flows in different European countries against the EU region. In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. This table reports estimations for Model D. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

| | |u i,t − u EU,t |

Intercept

0.0019166 −0.8443473 −0.0093622 −0.4633204 0.0038393 −0.6760852 −0.0021967 −0.4931877 (0.2309038)* (0.0031102) (0.2904626)** (0.0056319) (0.3365899) (0.0019324)* (0.1775904)*** (0.0025018) −0.0000087 0.0000081 0.0000442 0.0000397 0.0001581 0.0001263 0.0000459 0.0000407 (0.0002249) (0.0001783) (0.0001555) (0.0001645) (0.0001284) (0.0001372) (0.0001339) (0.0001355) | | |G D P i,t − G D P EU,t | 0.0000004 0.0000004 0.0000005 0.0000005 0.0000001 0.0000001 0.0000003 0.0000002 (0.0000002) (0.0000002) (0.0000002)** (0.0000002)** (0.0000001) (0.0000001) (0.0000001)*** (0.0000001)** | | SocSpe EU,t | | SocSpei,t 0.0002048 0.0002341 0.0002394 0.0003316 0.0002234 0.0002878 | G D P i,t − G D P EU,t | 0.0000599 0.0001249 (0.0003944) (0.0003734) (0.0003977) (0.0003900) (0.0002579) (0.0002642) (0.0003121) (0.0003238) Y ear t 0.0004209 0.0002264 0.0003381 0.0002456 (0.0001440)** (0.0001692) (0.0000884)*** (0.0001157)* EU i,t 0.0058401 0.0048512 0.0028191 0.0009698 (0.0034848) (0.0046417) (0.0023887) (0.0027781) E Ai,t 0.0061988 0.0058148 0.0035340 0.0027611 (0.0024294)* (0.0023397)* (0.0015895)* (0.0015186) R2 0.3303 0.3869 0.4981 0.5128 0.0925 0.2147 0.1813 0.2332 Adj. R2 0.3255 0.3811 0.4921 0.5059 0.0855 0.2067 0.171 0.2215 AIC −2929 −2965 −3048 −3058 −3049 −3127 −3177 −3183 BIC −2909 −2940 −3019 −3026 −3029 −3103 −3149 −3151 F 69.2*** 66.2*** 83.1*** 73.3*** 13.2*** 26.6*** 17.6*** 20.0*** DF 421 420 419 418 389 390 398 395

Table 3.8

3 LABOR MOBILITY, THE EMPIRICS

59

0.004598

0.01317

0.00787

R2

Adj. R2

0.004473

0.01305

0.007746

N et Mig i, j,t popi,t

−0.0000221000 (0.0000028587)*** 0.0000157732 (0.0000016130)*** 0.0000320595 (0.0000066398)***

Estimate

Removing outliers

0.00969

0.04155

0.02668

R2

0.009563

0.04143

0.02655

Adj. R2

This table reports simple regression estimations of net bilateral migration flows in different European countries against their peers (bilateral). For simplification reasons, we only report the results of the inclination coefficient. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

−0.0000319416 (0.0000085782)*** 0.0000226000 (0.0000042157)*** 0.000063356 (0.000020805)**

Estimate

Full sample

Simple regression—dependent variable:

SocSpei,t /G D P i,t SocSpe j,t /G D P j,t

G D P i,t G D P j,t

u i,t u j,t

Ind. Var

Table 3.9

60 J. KABDERIAN DREYER AND P. A. SCHMID

3

LABOR MOBILITY, THE EMPIRICS

61

Notice that bilateral levels of relative unemployment, relative social spending, or relative per capita GDP explain only a small part of the variance in net bilateral migration, as the R2s of the different simple regressions are relatively low. Estimates are, however, strongly significant even at the 0.1% level. They also exhibit the expected signs. Higher relative unemployment leads to net emigration, while relative per capita GDP and relative social spending lead to net immigration. 3.3.3.2 Multiple Regressions We repeat these estimations using all possible combinations of independent variables to explain net bilateral migration. Table 3.10 shows our estimation results. Our bilateral independent variables explain only a small part of the variance in bilateral migration (low R2s). Once again, we observe that when used together in the same model, relative social spending and relative per capita GDP lead to collinearity issues. Consequently, the significance of relative social spending disappears in these cases. Otherwise, all estimates are significant, and their directions are in line with our expectations: • Higher bilateral relative unemployment leads to a higher net bilateral emigration. This goes in line with the stabilization role migration is expected to have in counteracting asymmetric shocks. • A higher relative per capita GDP or relative social spending leads to higher bilateral net immigration. This sign can be interpreted as an “indirect redistribution effect” and goes in line with what we observed in the previous estimations.

3.3.4

Total Migration Flows—Country vs. Country (Bilateral Relations)

We wanted to test in the bilateral setting whether EU or EA membership could explain bilateral migration. Moreover, we wanted to test whether a tendency exists in the intensity of migration flows over time and whether the distance between countries also plays a role in the migration decision. With this in mind, we repeat our estimations using total flows of bilateral migration and then add these specific variables in the multiple regression setting.

0.01568 0.01543 −106,016 −105,988 63.6*** 7993

0.0000603 (0.0000207)** 0.01203 0.01178 −105,986 −105,958 48.6*** 7993

−0.0000311 (0.0000085)***

0.0000184 (0.0000207) 0.0134 0.0132 −105,998 −105,970 54.6*** 7993

0.0000272 (0.0000197) 0.0163 0.0159 −106,019 −105,984 44.2*** 7992 0.04747 0.04722 −120,001 −119,974 194.7*** 7815

−0.0000132 (0.0000030)***

D −0.0000234 (0.0000090)**

0.0000105 (0.0000072) 0.0470 0.0466 −119,983 −119,948 128.7*** 7819

0.0000147 0.0000108 (0.0000017)*** (0.0000017)*** 0.0000311 0.0000030 (0.0000068)*** (0.0000070) 0.03494 0.0397 0.03469 0.0395 −120,261 −120,083 −120,233 −120,055 141.5*** 161.7*** 7815 7811

−0.0000121 −0.0000209 (0.0000029)*** (0.0000030)***

0.0000208 0.0000158 0.0000125 (0.0000041)*** (0.0000048)*** (0.0000017)***

−0.0000209 (0.0000101)*

C −0.0000384 (0.0000081)

This table reports multiple regression estimations of net bilateral migration flows in different European countries against their country peers (bilateral). In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. This table reports estimations for these models. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

Adj. R2 AIC BIC F DF

R2

0.0000187 (0.0000050)***

−0.0000194 (0.0000102)

B −0.0000184 (0.0000087)*

A −0.0000167 (0.0000066)*

C D −0.0000739 −0.0000493 (0.0000265)** (0.0000321)

A −0.0000289 (0.0000232)

B −0.0000468 (0.0000320)

Removing outliers

N et Mig i, j,t popi,t

Full sample

Multiple regressions of Models A to D—dependent variable:

SocSpei,t /G D P i,t SocSpe j,t /G D P j,t

G D P i,t G D P j,t

u i,t u j,t

Intercept

Table 3.10

62 J. KABDERIAN DREYER AND P. A. SCHMID

3

LABOR MOBILITY, THE EMPIRICS

63

3.3.4.1 Simple Regressions Table 3.11 shows the estimates for our simple regressions, where total bilateral migration flows relative to the population of each country are explained by the same type of bilateral variables of our prior investigation, but now expressed in absolute differences instead of ratios. Notice that our simple regressions imply that all our independent variables expressed in absolute values for differences in unemployment, per capita GDP, and social spending explain literarily nothing of the variance of migration flows. The R2 is close to 0 and estimates are not statistically significant. 3.3.4.2 Multiple Regressions To check the influence of EU, EA membership, and distance between countries on migration and whether a time tendency exists, we combine our independent variables with control variables that allow us to account for these effects. Tables 3.12, 3.13, 3.14 and 3.15 show our estimation results for independent estimations of Models A to D. We find that the greater the distance between countries, the lower the total volume of bilateral migration. Moreover, there is an increasing tendency of bilateral migration over time. This coefficient is, however, not significant when we include the EU and EA membership variables. Likely, this happens because of a collinearity between the time effect and the countries becoming part of the EU and EA.

3.4

Discussion

According to our descriptive analysis, migration flows have been growing along our sample period in general. In the case of intra-EU migration, the increase in migration flows is much stronger. Specifically, intra-EU migration, compared to total migration, has been increasing sharply in the last 20 years, favoring the argument of an endogenous development of the EU as an optimal currency area. EU and EA memberships in themselves lead to an increase in intra-EU migration. Moreover, volatilities in migration flows indicate an increase in dynamism of migration overall, but especially in intra-EU migration flows. This could indicate that migration is increasing over time and is becoming a tool of the currency union to counteract asymmetric shocks.

j,t

0.0002

0.0018

0.0000

R2

V ol Mig i, j,t popi,t

0.0000

0.0016

0.0000

Adj. R2 −0.0000029 (0.0000023) 0.0000000 (0.0000000) 0.0000003 (0.0000032)

Estimate

Removing outliers

0.0000

0.0001

0.0010

R2

−0.0001

0.0000

0.0009

Adj. R2

This table reports simple regression estimations of total bilateral flows of migration in different European countries against their peers (bilateral). For simplification reasons, we only report the results of the inclination coefficient. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

i,t

| | SocSpe | | SocSpei,t − G D P j,t | | GDP

0.0000000 (0.0000061) 0.0000000 (0.0000000) 0.0000035 (0.0000081)

Estimate

Full sample

Simple regression—dependent variable:

| | |G D P i,t − G D P j,t |

| | |u i,t − u j,t |

Ind. Var

Table 3.11

64 J. KABDERIAN DREYER AND P. A. SCHMID

R2 Adj. R2 AIC BIC F DF

E Ai, j,t

EU i, j,t

Y ear t

Dist i, j

| | SocSpe | | SocSpei,t − G D P j,t | | GDP i,t j,t

0.0018 0.0015 −91,174 −91,146 7.3*** 7993

0.0002646 (0.0000447)*** −0.0000012 (0.0000062) 0.0000000 (0.0000000)

Full sample

0.0201 0.01969 −91,319 −91,277 41.1*** 7991

−0.0000001 (0.0000000)* 0.0000012 (0.0000037)

−0.0000001 (0.0000000)*

0.0201 0.0197 −91,320 −91,285 54.6*** 7992

−0.0020563 (0.0074175) 0.0000027 (0.0000066) 0.0000000 (0.0000000)

V ol Mig i, j,t popi,t

0.0004480 (0.0000821)*** 0.0000025 (0.0000065) 0.0000000 (0.0000000)

Multiple regression Model A—dependent variable:

| | |G D P i,t − G D P j,t |

| | |u i,t − u j,t |

Intercept

A

Table 3.12

0.0000778 (0.0000545) 0.0001293 (0.0000887) 0.0281 0.0275 −91,382 −91,333 46.3*** 7990

−0.0000001 (0.0000001)*

0.0003364 (0.0000913)*** 0.0000038 (0.0000068) 0.0000000 (0.0000000)

(continued)

−0.0000001 (0.0000001)* −0.0000042 (0.0000049) 0.0000964 (0.0000626) 0.0001399 (0.0001003) 0.0289 0.0282 −91,387 −91,331 39.7*** 7989

0.0087140 (0.0097261) 0.0000035 (0.0000069) 0.0000000 (0.0000000)

3 LABOR MOBILITY, THE EMPIRICS

65

(continued)

0.0010 0.0007 −101,400 −101,372 4.0* 7795

0.0002646 (0.0000447)*** −0.0000012 (0.0000062) 0.0000000 (0.0000000)

Removing outliers

0.0386 0.0382 −102,162 −102,127 104.2*** 7781

0.0435 0.0430 −102,502 −102,460 88.4*** 7771

−0.0000001 (0.0000000)*** 0.0000034 (0.0000016)*

−0.0000001 (0.0000000)***

0.0000000 (0.0000000)

−0.0064405 (0.0032271)* 0.0000010 (0.0000025) 0.0000000 (0.0000000)

0.0003303 (0.0000448)***

0.0000251 (0.0000261) 0.0000425 (0.0000275) 0.0443 0.0436 −102,777 −102,728 71.9*** 7765

−0.0000001 (0.0000000)***

0.0002824 (0.0000503)*** 0.0000011 (0.0000025) 0.0000000 (0.0000000)

−0.0000001 (0.0000000)*** 0.0000019 (0.0000019) 0.0000193 (0.0000270) 0.0000398 (0.0000317) 0.0475 0.0468 −102,885 −102,829 64.5*** 7755

−0.0034468 (0.0037501) 0.0000014 (0.0000025) 0.0000000 (0.0000000)

This table reports multiple regression estimations of total migration flows in different European countries against their country peers (bilateral). In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. This table reports estimations for Model A. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

R2 Adj. R2 AIC BIC F DF

E Ai, j,t

EU i, j,t

Y ear t

Dist i, j

| | SocSpe | | SocSpei,t − G D P j,t | | GDP i,t j,t

| | |G D P i,t − G D P j,t |

| | |u i,t − u j,t |

Intercept

A

Table 3.12

66 J. KABDERIAN DREYER AND P. A. SCHMID

R2 Adj. R2 AIC BIC F DF

E Ai, j,t

EU i, j,t

Y ear t

Dist i, j

i,t

j,t

0.0186 0.0183 −91,308 −91,273 50.6*** 7992

0.0000063 (0.0000080) −0.0000001 (0.0000000)*

0.0000035 (0.0000082)

0.0002 0.0000 −91,161 −91,133 0.8 7993

0.0004603 (0.0000978)*** 0.0000044 (0.0000066)

0.0002869 (0.0000564)*** 0.0000003 (0.0000062)

Full sample

Multiple regression Model B—dependent variable:

| | |G D P i,t − G D P j,t | | | SocSpe | | SocSpei,t − G D P j,t | | GDP

| | |u i,t − u j,t |

Intercept

B

Table 3.13

0.0187 0.0183 −91,307 −91,265 38.2*** 7991

0.0000063 (0.0000080) −0.0000001 (0.0000000)* 0.0000015 (0.0000037)

−0.0025498 (0.0075194) 0.0000045 (0.0000067)

V ol Mig i, j,t popi,t

0.0000285 (0.0000366) 0.0001208 (0.0000834) 0.0239 0.0233 −91,347 −91,299 39.2*** 7990

0.0000075 (0.0000079) −0.0000001 (0.0000000)*

0.0004103 (0.0000895)*** 0.0000055 (0.0000069)

(continued)

0.0000076 (0.0000078) −0.0000001 (0.0000000)* −0.0000019 (0.0000042) 0.0000350 (0.0000379) 0.0001250 (0.0000895) 0.0241 0.0233 −91,347 −91,291 32.8*** 7989

0.0041518 (0.0084034) 0.0000054 (0.0000070)

3 LABOR MOBILITY, THE EMPIRICS

67

j,t

(continued)

0.0385 0.0381 −102,548 −102,513 103.8*** 7766

0.0000031 (0.0000036) −0.0000001 (0.0000000)***

0.0000004 (0.0000033)

0.0006 0.0003 −101,778 −101,750 2.3 7772

0.0003020 (0.0000438)*** 0.0000011 (0.0000025)

0.0002076 (0.0000270)*** −0.0000021 (0.0000025)

Removing outliers

0.0450 0.0445 −102,853 −102,811 91.4*** 7756

0.0000035 (0.0000035) −0.0000001 (0.0000000)*** 0.0000039 (0.0000016)*

−0.0074655 (0.0031415)* 0.0000020 (0.0000026)

0.0000178 (0.0000237) 0.0000446 (0.0000289) 0.0452 0.0446 −102,972 −102,923 73.5*** 7754

0.0000039 (0.0000036) −0.0000001 (0.0000000)***

0.0002713 (0.0000433)*** 0.0000020 (0.0000026)

0.0000036 (0.0000034) −0.0000001 (0.0000000)*** 0.0000023 (0.0000018) 0.0000155 (0.0000249) 0.0000395 (0.0000314) 0.0498 0.0491 −103,136 −103,080 67.7*** 7740

−0.0043585 (0.0036303) 0.0000022 (0.0000025)

This table reports multiple regression estimations of total migration flows in different European countries against their country peers (bilateral). In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. This table reports estimations for Model B. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

R2 Adj. R2 AIC BIC F DF

E Ai, j,t

EU i, j,t

Y ear t

Dist i, j

i,t

| | |G D P i,t − G D P j,t | | | SocSpe | | SocSpei,t − G D P j,t | | GDP

| | |u i,t − u j,t |

Intercept

B

Table 3.13

68 J. KABDERIAN DREYER AND P. A. SCHMID

R2 Adj. R2 AIC BIC F DF

E Ai, j,t

EU i, j,t

Y ear t

Dist i, j

| | SocSpe | | SocSpei,t − G D P j,t | | GDP i,t j,t

0.0203 0.0199 −91,322 −91,287 55.2*** 7992

0.0000000 (0.0000000) 0.0000047 (0.0000082) −0.0000001 (0.0000000)*

0.0000000 (0.0000000) 0.0000026 (0.0000084)

0.0019 0.0016 −91,175 −91,147 7.6** 7993

0.0004359 (0.0000864)***

0.0002491 (0.0000532)***

Full sample

Multiple regression Model C—dependent variable:

| | |u i,t − u j,t | | | |G D P i,t − G D P j,t |

Intercept

C

Table 3.14

0.0203 0.0198 −91,320 −91,278 41.5*** 7991

0.0000000 (0.0000000) 0.0000047 (0.0000081) −0.0000001 (0.0000000)* 0.0000011 (0.0000036)

−0.0017245 (0.0072808)

V ol Mig i, j,t popi,t

0.0000686 (0.0000517) 0.0001344 (0.0000905) 0.0282 0.0276 −91,383 −91,334 46.4*** 7990

0.0000000 (0.0000000) 0.0000051 (0.0000080) −0.0000001 (0.0000001)*

0.0003327 (0.0000856)***

(continued)

0.0000000 (0.0000000) 0.0000051 (0.0000079) −0.0000001 (0.0000001)* −0.0000043 (0.0000048) 0.0000880 (0.0000612) 0.0001450 (0.0001002) 0.0290 0.0283 −91,388 −91,332 39.8*** 7989

0.0089043 (0.0095660)

3 LABOR MOBILITY, THE EMPIRICS

69

(continued)

0.0389 0.0386 −102,628 −102,593 104.9*** 7756

0.0000000 (0.0000000) 0.0000031 (0.0000033) −0.0000001 (0.0000000)***

0.0000000 (0.0000000) 0.0000008 (0.0000033)

0.0002 0.0000 −101,786 −101,758 0.7 7775

0.0003051 (0.0000463)***

0.0002026 (0.0000304)***

Removing outliers

0.0429 0.0424 −102,838 −102,796 86.9*** 7753

0.0000000 (0.0000000) 0.0000030 (0.0000034) −0.0000001 (0.0000000)*** 0.0000033 (0.0000015)*

−0.0063360 (0.0029642)*

0.0000194 (0.0000241) 0.0000464 (0.0000293) 0.0468 0.0462 −103,119 −103,070 76.1*** 7745

0.0000000 (0.0000000) 0.0000035 (0.0000035) −0.0000001 (0.0000000)***

0.0002682 (0.0000488)***

0.0000000 (0.0000000) 0.0000037 (0.0000035) −0.0000001 (0.0000000)*** 0.0000019 (0.0000018) 0.0000132 (0.0000250) 0.0000424 (0.0000304) 0.0478 0.0470 −103,031 −102,976 64.7*** 7743

−0.0035219 (0.0035603)

This table reports multiple regression estimations of total migration flows in different European countries against their country peers (bilateral). In order to be conservative and avoid misspecification or multicollinearity issues we estimate independently Models A to D with all possible combinations of independent variables. This table reports estimations for Model C. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

R2 Adj. R2 AIC BIC F DF

E Ai, j,t

EU i, j,t

Y ear t

Dist i, j

| | SocSpe | | SocSpei,t − G D P j,t | | GDP i,t j,t

| | |u i,t − u j,t | | | |G D P i,t − G D P j,t |

Intercept

C

Table 3.14

70 J. KABDERIAN DREYER AND P. A. SCHMID

R2 Adj. R2 AIC BIC F DF

E Ai, j,t

EU i, j,t

Y ear t

Dist i, j

| | SocSpe | | SocSpei,t − G D P j,t | | GDP i,t j,t

0.0019 0.0015 −91,173 −91,138 5.1** 7992

0.0002532 (0.0000551)*** −0.0000010 (0.0000063) 0.0000000 (0.0000000) 0.0000024 (0.0000085)

Full sample

0.0205 0.0200 −91,321 −91,280 41.8*** 7991

0.0004259 (0.0000858)*** 0.0000031 (0.0000066) 0.0000000 (0.0000000) 0.0000052 (0.0000083) −0.0000001 (0.0000000)*

Multiple regression Model D—dependent variable:

| | |G D P i,t − G D P j,t |

| | |u i,t − u j,t |

Intercept

D

Table 3.15

0.0206 0.0200 −91,320 −91,271 33.6*** 7990

−0.0020992 (0.0073751) 0.0000032 (0.0000067) 0.0000000 (0.0000000) 0.0000052 (0.0000082) −0.0000001 (0.0000000)* 0.0000013 (0.0000037)

V ol Mig i, j,t popi,t

39.3*** 7989

0.0000747 (0.0000549) 0.0001326 (0.0000890) 0.0286 0.0279

0.0003141 (0.0000908)*** 0.0000043 (0.0000068) 0.0000000 (0.0000000) 0.0000057 (0.0000081) −0.0000001 (0.0000001)*

(continued)

0.0086320 (0.0096086) 0.0000041 (0.0000069) 0.0000000 (0.0000000) 0.0000056 (0.0000080) −0.0000001 (0.0000001)* −0.0000042 (0.0000048) 0.0000932 (0.0000633) 0.0001431 (0.0000990) 0.0294 0.0286 −91,389 −91,326 34.6*** 7988

3 LABOR MOBILITY, THE EMPIRICS

71

(continued)

0.0008 0.0004 −101,492 −101,457 2.1 7780

0.0002105 (0.0000329)*** −0.0000021 (0.0000024) 0.0000000 (0.0000000) 0.0000012 (0.0000035)

Removing outliers

0.0395 0.0390 −102,460 −102,418 80.0 7769

0.0003055 (0.0000492)*** 0.0000010 (0.0000025) 0.0000000 (0.0000000) 0.0000038 (0.0000037) −0.0000001 (0.0000000)***

0.0436 0.0430 −102,608 −102,559 70.8*** 7764

−0.0063685 (0.0032412)* 0.0000015 (0.0000026) 0.0000000 (0.0000000) 0.0000036 (0.0000037) −0.0000001 (0.0000000)*** 0.0000033 (0.0000016)* 0.0000208 (0.0000239) 0.0000468 (0.0000289) 0.0456 0.0449 −102,945 −102,889 61.8*** 7756

0.0002592 (0.0000498)*** 0.0000018 (0.0000025) 0.0000000 (0.0000000) 0.0000043 (0.0000036) −0.0000001 (0.0000000)***

−0.0042529 (0.0037625) 0.0000020 (0.0000025) 0.0000000 (0.0000000) 0.0000041 (0.0000035) −0.0000001 (0.0000000)*** 0.0000023 (0.0000019) 0.0000146 (0.0000258) 0.0000415 (0.0000309) 0.0501 0.0492 −103,027 −102,964 58.3*** 7747

This table reports multiple regression estimations of total migration flows in different European countries against their country peers (bilateral). In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. This table reports estimations for Model D. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

R2 Adj. R2 AIC BIC F DF

E Ai, j,t

EU i, j,t

Y ear t

Dist i, j

| | SocSpe | | SocSpei,t − G D P j,t | | GDP i,t j,t

| | |G D P i,t − G D P j,t |

| | |u i,t − u j,t |

Intercept

D

Table 3.15

72 J. KABDERIAN DREYER AND P. A. SCHMID

3

LABOR MOBILITY, THE EMPIRICS

73

Our regressions on EU net migration indicate that this variable works in smoothing unemployment in the different European countries. Moreover, migration reduces inequality between people of different countries because wealthier regions attract more immigrants than other regions. This is intensified even further in countries that spend relatively more in social benefits. We regress total migration volumes to show that a tendency toward stronger migration intensities exists over time. Here we also show that EA membership is an important stimulus to attract immigrants from other European countries. On the other hand, no significance is found for EU membership in this case. Our bilateral regressions indicate that only a small part of the variance in migration flows can be explained by bilateral comparisons (country vs. country). The estimates of our independent variables are significant and exhibit signs that go in line with those of our aggregate regressions: (1) Higher unemployment leads to higher net bilateral emigration; and (2) higher relative bilateral per capita GDP and higher relative bilateral social spending lead to higher bilateral net immigration. Finally, we regress total bilateral migration volumes to include other specific control variables. We learn that distance between countries reduces bilateral migration volumes. Moreover, a tendency in favor of bilateral migration exists over time within European countries. In summary, we find evidence that both the EU and EA have been developing over time toward higher intensity of migration flows. Moreover, these are working in the directions that we expect considering the migration role in stabilizing economic shocks and even on indirectly redistributing wealth between people of different regions.

References Alizadeh, S., Shahiki Tash, M. N., & Dreyer, J. K. (2021). Liquidity risk, transaction costs and financial closedness: Lessons from the Iranian and Turkish stock markets. Review of Accounting and Finance, 20(1), 84–102. Dreyer, J. K., Moreira, M., Smith, W., & Sharma, V. (2023). Do Environmental, social and governance practices affect portfolio returns? Evidence from the US stock market from 2002 to 2020. Review of Accounting and Finance, 22(1), 37–61. Dreyer, J. K., & Schmid, P. A. (2020). Optimal currency areas and the euro, Vol. I: Business cycles synchronization. Springer Nature.

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Dreyer, J. K., Schneider, J., & Smith, W. T. (2013). Saving-based asset pricing. Journal of Banking and Finance, 37 (9), 3704–3715. Dreyer, J. K., Schneider, J., & Smith, W. T. (2020). Saving-based asset pricing and leisure. Annals of Economics and Finance, 21(2), 507–526. Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(4), 1029–1054. Huart, F., & Tchakpalla, M. (2019). Labor market conditions and geographic mobility in the Eurozone. Comparative Economic Studies, 61, 263–284. Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. Stock, J. H., & Wright, J. H. (2000). GMM with weak identification. Econometrica, 68(5), 1055–1096.

CHAPTER 4

Capital Mobility, the Empirics

Abstract In this chapter, we conduct our quantitative analysis on capital mobility. Given data limitation, we had to restrict our analysis considerably. In terms of labor mobility, some of our research questions are analyzed using descriptive statistics and graphical analysis, while others make use of panel regression models. The chapter indicates a negative tendency in the volume of capital flows over the sample period. Differences in unemployment, wealth, and social benefits between countries can only marginally explain the volume in capital flows. Moreover, there might be regional bias (home bias) in the EA as we can observe a preference of investors to keep their wealth not only within the EA, but also as close as possible to their home countries. Keywords Optimal currency areas · Determinants of capital flows · Economic stabilization · EA · Regional and home bias · Investments

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kabderian Dreyer and P. A. Schmid, Optimal Currency Areas and the Euro, Volume II, https://doi.org/10.1007/978-3-031-38867-5_4

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4.1

Capital Mobility: Methodology of Analysis

We adopt the same strategy we used to study migration flows for the analysis of capital flows. The questions we could pose for capital flows in light of our study on migration are described in Sect. 2.7. However, we have a few data problems that limit our investigation on capital mobility: 1. Concerning the first and second questions on the increase of capital flows over time in the EU and their standard deviations, ideally we should analyze the flows by looking at the yearly differences in the change in capital liabilities and claims related to each specific country with the EU. Since data for capital mobility is restricted to just a few countries, especially at the start of our sample period, and since the number of countries included in our sample changes so much along the sample period, this type of empirical analysis would be not only data biased but also misleading. 2. Concerning the analysis of the volume of capital flows in the EU and its correlation with GDP growth and unemployment, data restrictions are again posing limitations for our analysis. A description of the data used in this chapter can be found in Appendix 3. Because of data limitation, we chose to analyze a few of the aspects of questions 1–3 by using bilateral data instead of aggregated country data and making use of graphical analysis. This is not optimal, because it would be important to check capital flows of a country, given their weight in the country’s total flows with the EU or with the total values with the rest of the world. To analyze the fourth research question, we could follow our migration analysis by looking at different variables of interest. Although our data does not allow us to do so, we should start by assuming that investors look at other European countries as a group and then analyze their relative economic situation. When a country is in a better state than the others, one could assume that assets in those other countries would be cheaper, opening great opportunities for capital allocation. Thus, we should regress the following model: N et Del Liai,t u i,t G D Pi,t = β0 + β1 + β2 G D PU Si,t u EU,t G D PEU,t SocSpei,t /G D Pi,t + β3 + εi,t , SocSpe EU,t /G D PEU,t

(4.1)

4

CAPITAL MOBILITY, THE EMPIRICS

77

where N et Del Liai,t is the difference between the total change in liabilities and claims of country i in year t measured in USD, u i,t is unemployment in country i in year t, u EU,t is the average EU unemployment in year t, G D PU Si,t is the total GDP in USD of country i in year t, G D Pi,t is the per capita GDP of country i in USD in year t, G D PEU,t is the average per capita GDP of EU countries in time t, SocSpei,t is the per capita social spending of country i in year t, and Social Spend EU,t is the average per capita social spending of the different EU countries in year t. If our data would allow, we would be able to use this first regression to check whether net capital flows in this aggregate form would respond to relative economic conditions between one country and all others. Should relative unemployment increase, for example, one could expect that capital would flow in the direction of the country with higher unemployment, smoothing the effect of the economic shock. Moreover, we would also expect—given the literature on beta convergence—that a currency union would motivate a higher inflow of capital from richer countries into poorer ones (measured by the ratio of per capita GDPs), as the latter ones should offer cheaper possibilities of investments or, in other words, investments with higher expected returns. Finally, we would also expect that capital would flow out of countries with higher standards of welfare, since these countries usually have higher taxes on capital. This first ideal analysis could be complemented by checking whether EU and EA memberships influence a higher intensity of capital flows. We could also test whether a time tendency on increase in capital mobility exists. In this case, we could regress the total volume of capital flows of a country in a specific year (sum of change in capital liabilities and claims) on the independent variables of Eq. 4.1 expressed in absolute differences: | | | | V ol DelCapi,t = β0 + β1 |u i,t − u EU,t | + β2 |G D Pi,t − G D PEU,t | G D PU Si,t | | | SocSpei,t SocSpe EU,t || + β3 || − G D Pi,t G D PEU,t | + β4 EUi,t + β5 E Ai,t + β6 Y eart + εi,t , (4.2) where V ol DelCapi,t is the sum of aggregate changes in capital liabilities and claims of country i in time t. The variables indicating relative unemployment, relative per capita GDP, and relative social spending are similar to those in Eq. 4.1, but now presented in absolute differences. This is done in order to cope with the positive characteristic of the dependent

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variable. EUi,t and E Ai,t are dummy variables that assume the values of 1 if a country is a member of the respective union, and 0 otherwise. The year counter is added to search for a yearly tendency in the intensity of capital flows. Similar to our migration analysis, we would expect differences in the three macro-independent variables to lead to higher intensity of capital flows. For example, differences in unemployment, wealth, or social subsidies should increase the flow of capital to those countries that are mostly affected by an economic shock, are relatively poorer, or have fewer taxes. Moreover, we would expect members of the EU and EA to receive more capital than others as they are part of a group that offers better conditions for investors in terms of both stability and transaction costs (in the case of the EA, no cost of currency exchange). As explained earlier, owing to data limitations on aggregate values of capital flows, we cannot conduct the regressions needed for Eqs. (4.1) and (4.2). We could, however, run a bilateral analysis of capital flows with the data available. It could be interesting, for example, to investigate whether investors can base their decisions on capital allocation using bilateral comparisons. For this case, we regress the following model: N et Del Liai, j,t u i,t G D Pi,t SocSpei,t /G D Pi,t = β0 + β1 + β2 + β3 + εi, j,t , G D PU Si,t u j,t G D P j,t SocSpe j,t /G D P j,t (4.3) where the subscripts “i, j ” refer to the bilateral relation between country i and country j, as in our analysis of migration flows. Equation (4.3) explains the net change in capital liabilities of one specific country i in relation to its GDP, comparing this country with another country j, instead of with averages of a union. With respect to the economic meaning of our variables, Eq. (4.3) follows Eq. (4.1), except that its variables are built relative to other countries instead of using union aggregates. Finally, also using the bilateral data on capital flows, we can test how membership in the EU and in the EA influences the intensity of capital flows, and whether a tendency toward increase in these flows exists over time. Moreover, as we did for labor mobility, we can test whether the distance between countries also influences the decision of investors to

4

CAPITAL MOBILITY, THE EMPIRICS

79

invest abroad. For this analysis, we can express the equation according to: | | | | V ol DelCapi, j,t = β0 + β1 |u i,t − u j,t | + β2 |G D Pi,t − G D P j,t | G D PU Si,t | | | SocSpei,t SocSpe j,t || | + β4 EUi, j,t + β3 | − G D Pi,t G D P j,t | + β5 E Ai, j,t + β6 Y eart + β7 Disti, j + εi, j,t ,

(4.4)

where the economic rationale follows that of Eq. (4.2). However, in this case, all variables are written in bilateral terms. The dummy variables EU and EA in Eq. (4.4) assume the value of 1 when both countries i and j are members of the respective union, and 0 otherwise. disti, j is the distance in kilometers between the capitals of countries i and j. We would expect greater distances between two countries to decrease bilateral capital flows. To be conservative and avoid model misspecification or problems of multicollinearity, we use all possible combinations of independent variables to explain bilateral capital flows. We applied the same procedure for labor mobility. Refer to Chapter 3 (empirics of labor mobility), Table 4.1, to see the different combinations of variables estimated in each regression model.1 We then calculate the variable estimates using the OLS2 method. We structure the chapter in the same way we did for labor mobility, where we start with a short and more general descriptive analysis, and then follow it with the econometrical work.

4.2

Descriptive Analysis

Given the challenges imposed by data on capital flows, it is hard to answer questions 1, 2, and 3. We decided to instead reflect on these questions by looking at the bilateral data we found available. This data allows us to construct Fig. 4.1. 1 We do not conduct a full VIF analysis as we did prior to the estimations of the first pivot (Dreyer and Schmid, 2020) because of the low number of independent variables in our models. 2 As in the case of labor mobility, we avoided the use of the GMM in our capital mobility estimations for two reasons: 1) It does not guarantee in itself that endogeneity issues will be corrected (Alizadeh et al., 2021; 2) the problem with weak instruments is that they often lead to weak identification (see Dreyer et al., 2013, 2020; Stock & Wright, 2000).

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Table 4.1 Simple regression—dependent variable: Full Sample Ind. Var

Estimate

u i,t u j,t G D Pi,t G D P j,t SocSpei,t /G D Pi,t SocSpe j,t /G D P j,t

0.0004056 (0.0005262) 0.0000218 (0.0000821) 0.0004301 (0.0010282)

N et Del Liai, j,t G D PU Si,t

Removing Outliers R2

Adj. R2

Estimate

0.0000 −0.0003173 (0.0001488)* 0.0000 −0.0001 0.0000898 (0.0000474) 0.0000 −0.0001 0.0001878 (0.0003118) 0.0000

R2

Adj. R2

0.0003

0.0002

0.0002

0.0000

0.0000 −0.0001

Table 4.1 reports simple regression estimations of net bilateral capital flows in different European countries against their peers (bilateral). For simplification reasons, we only report the results of the inclination coefficient. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers.

There is an indication that bilateral capital flows in the available countries have a very stable pattern over time, except for a counterintuitive decrease in volatility. Is this evidence that capital got more “stuck” over time in European countries? This would indicate that labor mobility became much more flexible with time in European countries compared to a stable capital mobility. It could also indicate that capital has always been free to move in these countries, while labor has not, giving the impression that the unions only facilitate labor mobility. But it is important to stress the limitations of our data: They are only available bilaterally and they are restricted to just a few countries, especially at the start of the sample period.

4.3

A Closer Look at Capital Flows

Since no aggregate data on capital flows per country were available, and given our bilateral data limitations, it is impossible for us to self-construct these aggregates. Unfortunately, we had to limit our econometrical analysis to the bilateral cases or, in other words, to the regressions of Eqs. (4.3) and (4.4).

Fig. 4.1 Bilateral capital mobility and its volatility over time (EU countries)

4 CAPITAL MOBILITY, THE EMPIRICS

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4.3.1

Net Capital Mobility—Country vs. Country (Bilateral Relations)

In this section, we investigate whether capital flows can be explained by bilateral references. Do macroeconomic imbalances between two countries justify capital flows? Our first analysis refers to net bilateral capital flows. Then we complement it with regressions to explain the total volumes of capital flows between two countries. As we did in our studies of labor mobility, we added the variables EU, EA, time tendency, and distance between countries as control variables in order to explain the volume of capital flows. 4.3.1.1 Simple Regressions Table 4.1 shows simple regression estimates where net change in bilateral liabilities relative to the size of GDP of each country i in each year t is explained by the bilateral relative independent variables of Eq. (4.3). Notice that bilateral levels of relative unemployment, relative social spending, or relative per capita GDP explain literally no variance in net bilateral capital flows, as R2 approaches 0. Estimates related to relative unemployment and relative wealth are significant at the 5 and 10% significance level, respectively. The signs of these coefficients are, however, in the opposite direction of what we would expect—the higher the relative unemployment, the more money leaves the country—exactly the opposite of what an efficient currency union would claim should happen, possibly amplifying asymmetric shocks. Moreover, richer countries tend to attract more capital—again the opposite of what one might have expected. However, given the very low R2 s, it could be that these variables are statistically but not economically significant. Notice that we systematically run our estimations first with the entire dataset and afterward removing outliers following the 4/n Cook’s distance criterion on all our regression residuals, as, for example, in Dreyer et al. (2023). For more details on the Cook’s distance, please refer to Appendix 4. 4.3.1.2 Multiple Regressions We again use the variables that explained net capital flows in the simple regressions, but now in a multiple regression setting. Table 4.2 shows our estimation results. Notice that results do not change much compared to the simple regressions. Our variables explain almost nothing of bilateral net capital inflows

C

D

0.0004829 0.0003851 (0.0005832) (0.0004964) 0.0000785 (0.0001044) 0.0003289 (0.0009607) 0.0000 0.0000 −0.0001 −0.0001 −26,323 −26,323 −26,295 −26,295 0.3 0.2 7154 7154

0.0004582 (0.0005396) 0.0000042 0.0000668 (0.0000992) (0.0001087) 0.0004240 0.0002118 (0.0011036) (0.0010034) 0.0000 0.0000 −0.0002 −0.0003 −26,322 −26,321 −26,295 −26,286 0.0 0.2 7154 7153

−0.0002172 −0.0002463 (0.0001762) (0.0001425) 0.0000474 0.0000610 (0.0000546) (0.0000511) 0.0002406 0.0000436 (0.0003223) (0.0003694) 0.0003 0.0002 0.0001 0.0000 0.0000 −0.0001 −40,606 −40,966 −40,480 −40,579 −40,938 −40,453 1.0 0.8 0.4 7029 7018 7035

−0.0003075 (0.0001695) 0.0000332 (0.0000539) 0.0002513 (0.0003565) 0.0004 0.0000 −40,299 −40,264 1.0 7032

Table 4.2 reports multiple regression estimations of net bilateral capital flows in different European countries against their country peers (bilateral). In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers.

R2 Adj. R2 AIC BIC F DF

B

−0.0015920 −0.0016914 −0.0013890 −0.0017927 −0.0002574 −0.0003777 −0.0005855 −0.0004170 (0.0012713) (0.0018854) (0.0015929) (0.0019136) (0.0004039) (0.0005667) (0.0005648) (0.0006665)

D

A

C

A

B

Removing Outliers

N et Del Liai, j,t G D PU Si,t

Full Sample

Multiple regressions of Models A to D—dependent variable:

SocSpei,t /G D Pi,t SocSpe j,t /G D P j,t

G D Pi,t G D P j,t

u i,t u j,t

Intercept

Table 4.2

4 CAPITAL MOBILITY, THE EMPIRICS

83

84

J. KABDERIAN DREYER AND P. A. SCHMID

as R2 s remain very low. In the multiple regression case, only the unemployment relation between two countries has a low statistical significance at the 10% level of significance—and again, with the “wrong sign.” One could argue that the reason for the wrong sign is that capital inflow caused by an asymmetric shock in a country that exhibits more economic problems during a recession is compensated by investors being scared to invest in that specific country. This goes in line with the theory of credit boom-bust cycles. According to this theory, “financial cycle booms produce financial imbalances in borrowers’ and lenders’ balance sheets, masked by higher asset prices and a temporary boost to output. Subsequently, a turn in the financial cycle results in a debt overhang and a higher probability of financial crisis , exacerbating the ensuing business cycle downturn and generating hysteresis, as deleveraging by firms and households coupled with credit contraction puts persistent downward pressure on incomes and private investment, ultimately lowering trend growth” (Franks et al., 2018, p. 15). However, since the R2s of these estimations approach 0, we can argue that even though we observe some statistical significance, very little can be said about the economic meaning of this coefficient. 4.3.2

Total Capital Flows—Country vs. Country (Bilateral Relations)

We would also like to test whether EU or EA membership can influence bilateral capital flows, and whether a tendency exists in the intensity of capital flows over time. Moreover, we would like to check whether the distance between two countries might influence the intensity of capital flows, with investors psychologically feeling safer to move capital to countries that are closer to them. With this in mind, we repeat our estimations using total bilateral capital flows and adding these specific variables in the multiple regression setting. 4.3.2.1 Simple Regressions Table 4.3 shows the estimation results for our simple regressions of the total volume of capital flows between two countries, explained by the same type of bilateral variables as in our prior investigation, but now expressed in absolute differences instead of ratios. Notice again that our independent variables explain literally none of the variance of the dependent variable. In this case, there are two statistically significant variables: unemployment differentials and social spending

4

CAPITAL MOBILITY, THE EMPIRICS

Table 4.3 Simple regression—dependent variable: Full Sample Ind. Var

Estimate

85

V ol DelCapi, j,t G D PU Si,t

Removing Outliers R2

Adj. R2

Estimate

R2

Adj. R2

| | |u i,t − u j,t |

−0.0006101 0.0014 0.0013 −0.0004217 0.0069 0.0068 (0.0002819)* (0.0000802)*** 0.0000003 0.0094 0.0093 0.0000000 0.0016 0.0014 (0.0000001)** (0.0000000) | SocSpe | − G D P j,t | −0.0019155*** 0.0088 0.0086 −0.0007504*** 0.0137 0.0135 j,t (0.0003648) (0.0001396)

| | |G D Pi,t − G D P j,t | | | SocSpei,t | GDP i,t

Table 4.3 reports simple regression estimations of total bilateral capital flows in different European countries against their peers (bilateral). For simplification reasons, we only report the results of the inclination coefficient. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers.

differentials. They are both significant at the 0.1% level. They, however, exhibit the “wrong sign”—with lower capital flows between countries that have the highest differences between unemployment and social benefits. We would expect these flows to be more intense exactly in countries with higher differences, as we argued before. This supports the boombust cycle theory. However, given the very low explanatory power of the model, one could argue that statistical significance in this case does not imply economic significance. 4.3.2.2 Multiple Regressions We would like to analyze the influence of EU, EA membership, and distance between countries on capital flows as well as whether a time tendency exists for these flows. Thus, we estimate Eq. (4.4) controlling for these variables using all possible combinations of relative independent variables. Tables 4.4, 4.5, 4.6, and 4.7 show our estimation results for independent estimations of Models A to D (see Table 3.1, Chapter 3). In all estimations, we can verify that our relative independent variables explain very little of the variance in the volume of capital flows, confirming what we observed in the simple regressions. However, the

R2 Adj. R2 AIC BIC

E Ai, j,t

EUi, j,t

Y eart

Disti, j

i,t

j,t

| | SocSpe | | SocSpei,t − G D P j,t | | GDP

0.0117 0.0115 −18,546 −18,518

0.0110383 (0.0028945)*** −0.0007767 (0.0002830)** 0.0000003 (0.0000001)**

Full Sample

0.0441 0.0437 −18,782 −18,748

−0.0000128 (0.0000022)***

0.0314555 (0.0044302)*** −0.0001594 (0.0002782) 0.0000003 (0.0000001)**

Multiple regression Model A—dependent variable:

| | |G D Pi,t − G D P j,t |

| | |u i,t − u j,t |

Intercept

A

Table 4.4

0.0475 0.0470 −18,805 −18,764

−0.0000127 (0.0000022)*** −0.0006453 (0.0002875)*

1.3270840 (0.5784062)* −0.0000914 (0.0002954) 0.0000003 (0.0000001)**

V ol DelCapi, j,t G D PU Si,t

0.0094396 (0.0045306)* 0.0134830 (0.0044704)** 0.0605 0.0598 −18,902 −18,853

−0.0000137 (0.0000023)***

0.0187150¨ (0.0065917)** −0.0002749 (0.0002638) 0.0000004 0.0000001)**

−0.0000137 (0.0000023)*** −0.0012213 (0.0004074)** 0.0147340 (0.0055299)** 0.0149981 (0.0047342)** 0.0712 0.0704 −18,982 −18,927

2.4656518 (0.8143743)** −0.0001483 (0.0002843) 0.0000004 (0.0000001)**

86 J. KABDERIAN DREYER AND P. A. SCHMID

E Ai, j,t

EUi, j,t

Y eart

Disti, j

| | SocSpe | | SocSpei,t − G D P j,t | | GDP i,t j,t

| | |G D Pi,t − G D P j,t |

| | |u i,t − u j,t |

Intercept

A

F DF

A

0.0115202 (0.0010132)*** −0.0003809 (0.0000814)*** 0.0000000 (0.0000000)

Removing Outliers

42.6*** 7154

Full Sample

0.2399197 (0.1235004) −0.0001343 (0.0000816) 0.0000000 (0.0000000)

−0.0000050 (0.0000005)*** −0.0001098 (0.0000615)

−0.0000049 (0.0000005)***

89.2*** 7152

0.0192993 (0.0013915)*** −0.0001486 (0.0000773) 0.0000000 (0.0000000)

110.2*** 7153

−0.0000054 (0.0000005)*** 0.0036240 (0.0011327)*** 0.0044255 (0.0012687)***

0.0145651 (0.0016823)*** −0.0001732 (0.0000776)* 0.0000000 (0.0000000)

92.1*** 7151

(continued)

−0.0000055 (0.0000006)*** −0.0003413 (0.0000778)*** 0.0054130 (0.0012709)*** 0.0051998 (0.0013092)***

0.6981120 (0.1556242)*** −0.0001443 (0.0000777) 0.0000000 (0.0000000)

91.4*** 7150

4 CAPITAL MOBILITY, THE EMPIRICS

87

(continued)

0.0082 0.0079 −35,308 −35,281 28.9*** 6995

Removing Outliers 0.0625 0.0620 −35,594 −35,560 155.5*** 6998

0.0633 0.0628 −35,371 −35,330 118.4*** 7003

0.0827 0.0820 −35,521 −35,473 126.3*** 7004

0.0944 0.0937 −35,500 −35,445 121.8*** 7004

Table 4.4 reports multiple regression estimations of total capital flows in different European countries against their country peers (bilateral). In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. Table 4.4 reports estimations for Model A. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers

R2 Adj. R2 AIC BIC F DF

A

Table 4.4

88 J. KABDERIAN DREYER AND P. A. SCHMID

R2 Adj. R2 AIC BIC

E Ai, j,t

EUi, j,t

Y eart

Disti, j

i,t

j,t

0.0413 0.0409 −18,761 −18,727

−0.0015777 (0.0003512)*** −0.0000125 (0.0000023)***

−0.0020058 (0.0003803)***

0.0110 0.0107 −18,540 −18,513

0.0465948 (0.0062200)*** −0.0001382 (0.0003044)

0.0295858 (0.0039407)*** −0.0007568 (0.0002912)**

Full Sample

Multiple regression Model B—dependent variable:

| | |G D Pi,t − G D P j,t | | | SocSpe | | SocSpei,t − G D P j,t | | GDP

| | |u i,t − u j,t |

Intercept

B

Table 4.5

0.0442 0.0437 −18,781 −18,739

−0.0015561 (0.0003468)*** −0.0000124 (0.0000022)*** −0.0005942 (0.0002840)*

1.2396796 (0.5728070)* −0.0000703 (0.0003240)

V ol DelCapi, j,t G D PU Si,t

0.0046579 (0.0038225) 0.0109950 (0.0044907)* 0.0501 0.0494 −18,822 −18,774

−0.0013437 (0.0003683)*** −0.0000133 (0.0000024)***

0.0397171 (0.0065922)*** −0.0001794 (0.0003002)

(continued)

−0.0012651 (0.0003705)*** −0.0000133 (0.0000024)*** −0.0009725 (0.0003750)** 0.0084048 (0.0043841) 0.0121555 (0.0047196)* 0.0570 0.0562 −18,873 −18,818

1.9891650 (0.7525381)** −0.0000522 (0.0003282)

4 CAPITAL MOBILITY, THE EMPIRICS

89

j,t

(continued)

E Ai, j,t

EUi, j,t

Y eart

Disti, j

i,t

| | |G D Pi,t − G D P j,t | | | SocSpe | | SocSpei,t − G D P j,t | | GDP

| | |u i,t − u j,t |

Intercept

B

F DF

B

Table 4.5

0.0223310 (0.0016183)*** −0.0002301 (0.0000824)** −0.0006916 (0.0001405)*** −0.0000049 (0.0000005)***

−0.0008278 (0.0001437)***

102.9*** 7153

0.0155475 (0.0012701)*** −0.0004715 (0.0000877)***

Removing Outliers

40.0*** 7154

Full Sample

−0.0007543 (0.0001507)*** −0.0000050 (0.0000006)*** −0.0001116 (0.0000638)

0.2472009 (0.1284006) −0.0002284 (0.0000909)*

82.8*** 7152

0.0036253 (0.0010679)*** 0.0040973 (0.0013258)**

−0.0006327 (0.0001467)*** −0.0000053 (0.0000006)***

0.0187398 (0.0015784)*** −0.0002254 (0.0000843)**

75.4*** 7151

−0.0006013 (0.0001465)*** −0.0000053 (0.0000006)*** −0.0002974 (0.0000727)*** 0.0047824 (0.0011413)*** 0.0046365 (0.0013283)***

0.6145332 (0.1459402)*** −0.0001767 (0.0000842)*

72.0*** 7150

90 J. KABDERIAN DREYER AND P. A. SCHMID

0.0226 0.0223 −34,329 −34,301 81.2*** 7018

Removing Outliers 0.0695 0.0691 −34,823 −34,789 174.8 7015

0.0705 0.0700 −34,284 −34,243 133.3*** 7023

0.0911 0.0904 −34,960 −34,912 140.7*** 7017

0.0994 0.0986 −35,227 −35,172 129.0*** 7011

Table 4.5 reports multiple regression estimations of total capital flows in different European countries against their country peers (bilateral). In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. Table 4.5 reports estimations for Model B. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers.

R2 Adj. R2 AIC BIC F DF

B

4 CAPITAL MOBILITY, THE EMPIRICS

91

R2 Adj. R2 AIC BIC

E Ai, j,t

EUi, j,t

Y eart

Disti, j

| | SocSpe | | SocSpei,t − G D P j,t | | GDP i,t j,t

0.0495 0.0491 −18,822 −18,788

0.0000003 (0.0000001)** −0.0015110 (0.0003376)*** −0.0000125 (0.0000022)***

0.0000003 (0.0000001)** −0.0018626 (0.0003618)***

0.0177 0.0175 −18,589 −18,562

0.0376730 (0.0048932)***

0.0173607 (0.0031534)***

Full Sample

Multiple regression Model C—dependent variable:

| | |u i,t − u j,t | | | |G D Pi,t − G D P j,t |

Intercept

C

Table 4.6

0.0528 0.0523 −18,845 −18,804

0.0000003 (0.0000001)** −0.0014973 (0.0003346)*** −0.0000123 (0.0000021)*** −0.0006404 (0.0002811)*

1.3233998 (0.5657989)*

V ol DelCapi, j,t G D PU Si,t

0.0097223 (0.0044594)* 0.0119685 (0.0047531)* 0.0636 0.0630 −18,926 −18,877

0.0000004 (0.0000001)** −0.0012110 (0.0003600)*** −0.0000135 (0.0000023)***

0.0236872 (0.0069059)***

0.0000004 (0.0000001)** −0.0011209 (0.0003653)** −0.0000134 (0.0000023)*** −0.0012020 (0.0004035)** 0.0148399 (0.0053980)** 0.0136784 (0.0050798)** 0.0740 0.0733 −19,004 −18,949

2.4317309 (0.8063902)**

92 J. KABDERIAN DREYER AND P. A. SCHMID

E Ai, j,t

EUi, j,t

Y eart

Disti, j

| | SocSpe | | SocSpei,t − G D P j,t | | GDP i,t j,t

| | |u i,t − u j,t | | | |G D Pi,t − G D P j,t |

Intercept

C

F DF

C

0.0214365 (0.0014366)*** 0.0000000 (0.0000000) −0.0006222 (0.0001272)*** −0.0000048 (0.0000005)***

0.0000000 (0.0000000) −0.0007072 (0.0001286)***

124.2*** 7153

0.0136194 (0.0011171)***

Removing Outliers

64.7*** 7154

Full Sample

0.0000000 (0.0000000) −0.0006595 (0.0001321)*** −0.0000049 (0.0000005)*** −0.0001140 (0.0000603)

0.2507061 (0.1211694)*

99.7*** 7152

0.0037088 (0.0010970)*** 0.0036601 (0.0012505)**

0.0000000 (0.0000000) −0.0005420 (0.0001334)*** −0.0000052 (0.0000005)***

0.0165842 (0.0017038)***

97.2*** 7151

(continued)

0.0000000 (0.0000000) −0.0005410 (0.0001334)*** −0.0000053 (0.0000005)*** −0.0003286 (0.0000747)*** 0.0054772 (0.0012364)*** 0.0045732 (0.0012982)***

0.6744142 (0.1494153)***

95.3*** 7150

4 CAPITAL MOBILITY, THE EMPIRICS

93

(continued)

0.0149 0.0146 −35,099 −35,072 53.0*** 7000

Removing Outliers 0.0707 0.0703 −35,728 −35,693 177.7*** 7000

0.0720 0.0715 −35,378 −35,337 136.0*** 7006

0.0881 0.0874 −35,733 −35,685 135.3*** 7003

0.1006 0.0998 −35,694 −35,639 130.6*** 7003

Table 4.6 reports multiple regression estimations of total capital flows in different European countries against their country peers (bilateral). In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. Table 4.6 reports estimations for Model C. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers.

R2 Adj. R2 AIC BIC F DF

C

Table 4.6

94 J. KABDERIAN DREYER AND P. A. SCHMID

R2 Adj. R2 AIC BIC

E Ai, j,t

EUi, j,t

Y eart

Disti, j

i,t

j,t

| | SocSpe | | SocSpei,t − G D P j,t | | GDP

0.0210 0.0206 −18,611 −18,576

0.0212200 (0.0038875)*** −0.0009179 (0.0002942)** 0.0000003 (0.0000001)** −0.0019690 (0.0003779)***

Full Sample

0.0498 0.0493 −18,823 −18,781

0.0384716 (0.0051159)*** −0.0003017 (0.0002899) 0.0000003 (0.0000001)** −0.0015541 (0.0003530)*** −0.0000122 (0.0000022)***

Multiple regression Model D—dependent variable:

| | |G D Pi,t − G D P j,t |

| | |u i,t − u j,t |

Intercept

D

Table 4.7

0.0530 0.0524 −18,845 −18,797

1.3011887 (0.5807126)* −0.0002333 (0.0003076) 0.0000003 (0.0000001)** −0.0015309 (0.0003489)*** −0.0000121 (0.0000021)*** −0.0006290 (0.0002888)*

V ol DelCapi, j,t G D PU Si,t

0.0094651 (0.0045099)* 0.0123041 (0.0046804)** 0.0641 0.0634 −18,928 −18,872

0.0247702 (0.0072689)*** −0.0003780 (0.0002771) 0.0000004 (0.0000001)** −0.0012597 (0.0003743)*** −0.0000131 (0.0000023)***

(continued)

2.4048646 (0.8242246)** −0.0002461 (0.0002999) 0.0000004 (0.0000001)** −0.0011536 (0.0003794)** −0.0000132 (0.0000023)*** −0.0011882 (0.0004126)** 0.0146139 (0.0055172)** 0.0138774 (0.0049740)** 0.0743 0.0734 −19,003 −18,941

4 CAPITAL MOBILITY, THE EMPIRICS

95

(continued)

E Ai, j,t

EUi, j,t

Y eart

Disti, j

| | SocSpe | | SocSpei,t − G D P j,t | | GDP i,t j,t

| | |G D Pi,t − G D P j,t |

| | |u i,t − u j,t |

Intercept

D

F DF

D

Table 4.7

0.0154902 (0.0013266)*** −0.0004309 (0.0000862)*** 0.0000000 (0.0000000) −0.0007814 (0.0001373)***

Removing Outliers

51.2*** 7153

Full Sample

0.0222617 (0.0015594)*** −0.0001818 (0.0000840)* 0.0000000 (0.0000000) −0.0006431 (0.0001341)*** −0.0000048 (0.0000005)***

93.7*** 7152

0.2425037 (0.1262494) −0.0001943 (0.0000858)* 0.0000000 (0.0000000) −0.0006851 (0.0001391)*** −0.0000048 (0.0000005)*** −0.0001096 (0.0000628)

80.1 7151

0.0036535 (0.0011575)** 0.0041577 (0.0013191)**

0.0174127 (0.0018413)*** −0.0002281 (0.0000817)** 0.0000000 (0.0000000) −0.0005818 (0.0001387)*** −0.0000052 (0.0000006)***

81.7*** 7150

0.6665804 (0.1547079)*** −0.0001886 (0.0000813)* 0.0000000 (0.0000000) −0.0005691 (0.0001384)*** −0.0000052 (0.0000005)*** −0.0003243 (0.0000774)*** 0.0053264 (0.0012779)*** 0.0046775 (0.0013051)***

81.9*** 7149

96 J. KABDERIAN DREYER AND P. A. SCHMID

0.0233 0.0229 −35,116 −35,082 55.7*** 7002

Removing Outliers 0.0714 0.0709 −35,343 −35,302 134.7*** 7005

0.0737 0.0730 −35,226 −35,178 111.5*** 7009

0.0910 0.0903 −35,322 −35,267 117.1*** 7009

0.1024 0.1015 −35,594 −35,532 114.2*** 7004

Table 4.7 reports multiple regression estimations of total capital flows in different European countries against their country peers (bilateral). In order to be conservative and avoid misspecification or multicollinearity issues, we estimate independently Models A to D with all possible combinations of independent variables. Table 4.7 reports estimations for Model D. Standard errors are written in parentheses and the symbols “***”, “**”, “*”, and “.” refer to statistical significance at 0.001, 0.01, 0.05, and 0.10, respectively. All standard errors and statistical tests are calculated according to a heteroscedasticity and autocorrelation consistent covariance matrix following Newey and West (1987). Moreover, the table reports both results with full sample and after removing outliers.

R2 Adj. R2 AIC BIC F DF

D

4 CAPITAL MOBILITY, THE EMPIRICS

97

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J. KABDERIAN DREYER AND P. A. SCHMID

new control variables—distance, EU, EA, and time trend—are all statistically significant and improve R2 considerably. Thus, we conclude the following: • The distance between two countries proves to be a key variable in determining capital flows: It is negative and statistically significant at the 0.1% level. Moreover, this variable inflates the R2 significantly. The more distant a country is from another, the less likely money flows between the pair of countries. This can be seen as a type of “home bias” (Coeurdacier & Rey, 2012; Lewis, 1999). • The time trend is also significant at the 0.1% level and negative. During the sample period, there is evidence that the intensity of capital flows between European countries decreased. In other words, along the time of our sample, investors became less likely to move their savings across borders. • EU and AZ memberships are also key determinants of the intensity in capital flows. These variables are positive and significant at the 0.1% level. EU and EA investors are more prone to investing their savings in other countries when these are also members of the unions.

4.4

Discussion

Our analysis of capital flows is unfortunately limited by data availability. We could only collect bilateral data between European countries. Data on very few countries was available at the start of our sample period. This improved along time, but remained far from optimal for the past few years. Thus, capital mobility could only be analyzed in the bilateral setting and for a restricted number of countries. We would expect capital flows to depend on the relative economic variables unemployment, wealth, and social spending. However, our results indicate that these variables explain literally no variance in capital flows both in the net and volume perspectives. However, when we control for other important variables such as time trend and EU and EA memberships, as well as distance, the intensity of capital flows can be partly explained. Thus: (1) EU and EA investors are more prone to investing their savings in countries that are partners in

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the membership, which could be explained by the lack of exchange rate differences (EA) or simply by lower transaction costs (both EU and EA); (2) investors tend to consider the distance they are from their investments. The further away a country is from the country of the investor, the less likely it is that capital flows between these countries. In the literature, this can be seen as a type of home bias; (3) there is a decreasing time trend for capital flows across the countries analyzed along our sample period. Since our analysis of capital mobility within European countries has so many data limitations, we consider it imperative that this study should be extended in the future with more data. As we did for labor mobility, it is of fundamental importance to analyze how bilateral capital flows respond to economic conditions. This should be done using both a more general aggregate analysis where the total amount of capital flows of each country can be measured, and also using a more complete bilateral analysis where data on more pairs of countries is available.

References Alizadeh, S., Shahiki Tash, M. N., & Dreyer, J. K. (2021). Liquidity risk, transaction costs and financial closedness: Lessons from the Iranian and Turkish stock markets. Review of Accounting and Finance, 20(1), 84–102. Coeurdacier, N., & Rey, H. (2012). Home bias in open economy financial macroeconomics. Journal of Economic Literature, 51(1), 63–115. Dreyer, J. K., Moreira, M., Smith, W., & Sharma, V. (2023). Do environmental, social and governance practices affect portfolio returns? Evidence from the US stock market from 2002 to 2020. Review of Accounting and Finance, 22(1), 37–61. Dreyer, J. K., & Schmid, P. A. (2020). Optimal currency areas and the euro, Vol. I: Business cycles synchronization. Springer Nature. Dreyer, J. K., Schneider, J., & Smith, W. T. (2013). Saving-based asset pricing. Journal of Banking and Finance, 37 (9), 3704–3715. Dreyer, J. K., Schneider, J., & Smith, W. T. (2020). Saving-based asset pricing and leisure. Annals of Economics and Finance, 21(2), 507–526. Franks, J. R., Barkbu, B. B., Blavy, R., Oman, W., & Schoelermann, H. (2018). Economic convergence in the Euro Area: Coming together or drifting apart? (IMF Working Paper, No. 18/10). Lewis, K. K. (1999). Trying to explain home bias in equities and consumption. Journal of Economic Literature, 37 (2), 571–608. Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. Stock, J. H., & Wright, J. H. (2000). GMM with weak identification. Econometrica, 68(5), 1055–1096.

CHAPTER 5

Conclusion

Abstract This chapter provides final discussions as well as concludes our pivot by discussing the results of our empirical analysis in light of the OCA theory and literature. There is a lack of agreement in the literature on whether the level of intra-EA factor mobility justifies sharing a common currency. Given an endogenous development of the currency union along the sample period toward higher levels of migration, we show with our analysis that the different conclusions of the literature can be strictly dependent on the sample period selected. Thus, we defend that our research work with this pivot to some extent reconciles the literature on labor mobility in the EA. Keywords EA · Migration · Capital flows · Optimal currency areas · Stabilization · Economic crisis

It is our main objective with this pivot series to analyze to what extent the EA can be considered an optimal currency area. To do so, we divided the series into three pivots according to the following OCA criteria: business cycle synchronization (Dreyer & Schmid, 2020), factor mobility, and the existence of fiscal federalism. This book studies factor mobility.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kabderian Dreyer and P. A. Schmid, Optimal Currency Areas and the Euro, Volume II, https://doi.org/10.1007/978-3-031-38867-5_5

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Bentivogli and Pagano (1999) defend that in the EA-11, migration of workers did not respond to unemployment shocks as in the United States. Puhani (2001) shares this pessimism as he shows that even within Germany, the labor market could not accommodate regional shocks. Obstfeld and Peri (1998) and Decressin and Fatás (1995) also defend that prior to the introduction of the Euro, the role of migration was negligible in terms of counteracting economic shocks. Working with very short sample periods, Modestino and Dennett (2018) and Puiu (2011) are also pessimists on the capabilities of migration to counteract asymmetric economic shocks in Europe. When considering a long sample period covering both the pre- and post-Euro introduction periods, Beyer and Smets (2015) defend that adjustments in unemployment rates through migration take twice as long in the EU than in the United States. Also using a long sample period, Dorn and Zweimüller (2021) defend the nonexistence of a common European labor market. For more recent sample periods, House et al. (2018) show that migration works in favor of diminishing unemployment differentials in the EU. Huart and Tchakpalla (2019), however, claim that low labor mobility in the EA responds to national differences in unemployment. Arpaia et al. (2016) defend that migration has doubled since the introduction of the Euro. The authors defend that real wages respond to labor mobility in the EU at the same levels as seen in the United States. Our results are in line with those of these authors. We find evidence of a strong increase in migration within workers of EU countries in our sample period, using data relating to the era after the introduction of the Euro. We show that migration rates increase overall, and also when we consider migration from non-EU countries. However, intra-EU migration increased relatively more significantly. This seems to support an endogenous development of the currency union toward the concept of optimal currency areas: Membership itself leads to increasing migration. In line with this conclusion, volatilities in migration flows in our sample also indicate an increase in dynamism of migration overall, but especially within EU countries. This leads us to conclude that migration in the EA works as a mechanism to counteract economic shocks. Our regressions on migration confirm these findings. They indicate that migration plays a significant role in counteracting unemployment differentials within EU countries. Moreover, they also show a redistributive effect of migration, as well as a higher intensity of migration over

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time (time trend). Euro membership is associated with higher intensity of intra-EU migration. Our regressions also show that migration should be considered in an aggregate way, where migrants compare their home situation to the situation applying to the entire union of countries and not to individual countries (aggregate vs. bilateral). Bilateral migration also reacts to bilateral relative unemployment, wealth, and social spending. However, these bilateral effects explain very little (if any) of the variance in bilateral migration. More specifically, bilateral migration volumes can be explained by the geographical location of countries, with higher migration flows between neighbor countries. The literature seems to be divided, pointing in different directions regarding the role of migration in counteracting unemployment differentials. The same applies to its role in fostering a better distribution of wealth in the EU. Thus, this pivot to some extent reconciles the literature by stressing the importance of the sample period collected. Prior to the Euro introduction, one would be skeptical about labor mobility within Europe. However, EU—and especially Euro membership—acted as an endogenous determinant of migration. Thus, articles with more recent data find results that put Euro countries much closer to sharing an optimal currency area. When it comes to capital flows, there is an overall paucity of studies. In general, the few articles we found have limited availability of data. Drakos et al. (2018) show that only a low degree of capital mobility can be observed in Europe. Abiad et al. (2009) found evidence for only downhill capital mobility (from richer to poorer countries). Canale et al. (2018) discuss the trade-off between free capital mobility and financial stability within eleven EA members. In line with the literature, we faced data restrictions that somewhat limit our conclusions. We could only find bilateral data on capital mobility between a few European countries, with most sample points only available in the most recent years. Relative bilateral unemployment, wealth, and social spending only marginally explain our data on bilateral capital flows. No aggregated data on flows could be used, as in the case of our migration analysis. We can, however, notice a higher inclination of European investors to move their savings to other member countries (especially to neighbor countries), possibly owing to exchange rate security, lower transaction costs, or even a type of home (regional) bias. Nevertheless, a negative time tendency toward capital mobility exists within the sampled countries. Likely there will be more datasets available on capital flows

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within European countries in the future. This should open new possibilities for the development of research on capital mobility, which we consider to be at an incipient stage.

References Abiad, A., Leigh, D., & Mody, A. (2009). Financial integration, capital mobility, and income convergence. Economic Policy, 24(58), 241–305. Arpaia, A., Kiss, A., Palvolgyi, B., & Turrini, A. (2016). Labour mobility and labour market adjustment in the EU. IZA Journal of Migration, 5, Article 21. Bentivogli, C., & Pagano, P. (1999). Regional disparities and labour mobility: The Euro-11 versus the USA. Labour, 13(3), 737–760. Beyer, R. C. M., & Smets, F. (2015). Labour market adjustments and migration in Europe and the United States: How different? Economic Policy, 30(84), 643–682. Canale, R. R., De Grauwe, P., Foresti, P., & Napolitano, O. (2018). Is there a trade-off between free capital mobility, financial stability and fiscal policy flexibility in the EMU? Review of World Economics, 154, 177–201. Decressin, J., & Fatás, A. (1995). Regional labor market dynamics in Europe. European Economic Review, 39(9), 1627–1655. Dorn, D., & Zweimüller, J. (2021). Migration and labor market integration in Europe. Journal of Economic Perspectives, 35(2), 49–76. Drakos, A. A., Kouretas, G. P., & Vlamis, P. (2018). Saving, investment and capital mobility in EU member countries: A panel data analysis of the Feldstein-Horioka puzzle. Applied Economics, 50(34–35), 3798–3811. Dreyer, J. K., & Schmid, P. A. (2020). Optimal currency areas and the Euro, Vol. I: Business cycles synchronization. Springer Nature. House, C. L., Proebsting, C., & Tesar, L. L. (2018). Quantifying the benefits of labor mobility in a currency union (NBER Working Paper Series, No. 25347). Huart, F., & Tchakpalla, M. (2019). Labor market conditions and geographic mobility in the Eurozone. Comparative Economic Studies, 61, 263–284. Modestino, A. S., & Dennett, J. (2018). Are American homeowners locked into their houses? The impact of housing market conditions on state-to-state migration. (Federal Reserve Bank of Boston Working Papers, No. 12–1). Obstfeld, M., & Peri, G. (1998). Regional nonadjustment and fiscal policy. Economic Policy, 13(26), 205–259. Puhani, P. (2001). Labour mobility: An adjustment mechanism in Euroland? Empirical evidence for Western Germany, France and Italy. German Economic Review, 2(2), 127–140. Puiu, C. (2011). Labour mobility as an adjustment mechanism in the Euro area. CES Working Papers, 3(4), 579–591.

Appendix 1: Summary of Labor Mobility Literature

See Table A.1. Table A.1Summary of the literature on labor mobility Article

Arpaia et al. (2016)

Basso et al. (2019)

Bentivogli & Pagano (1999)

Research questions

What is the role of labor mobility in the case of asymmetric shocks in the EU?

How does labor mobility absorb asymmetric shocks? Is labor mobility in the EA comparable to that in the United States?

Data

1970–2013, 26 EU countries (Bulgaria and Romania excluded)

2007–2016, EA countries, United States

Method

Vector autoregression

Regression analysis with year and region (country) fixed effects. Instrumental variable: local sector-specific changes in labor demand

Is net migration comparable between the EA and the United States? How responsive is net migration to disparities in income and shocks? 1981–1994, EA-11 with 44 regions, United States with 50 states and D.C Estimation of theoretical overlapping generations model for choice under uncertainty. Control variables: consumption flows, location; 2SLS with fixed and random effects (continued)

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kabderian Dreyer and P. A. Schmid, Optimal Currency Areas and the Euro, Volume II, https://doi.org/10.1007/978-3-031-38867-5

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(continued) Article

Arpaia et al. (2016)

Basso et al. (2019)

Main variables

Wages, employment, unemployment, participation rates

Population, employment, place of birth (native, foreign inside EU, foreign outside EU)

Results

Movements doubled since the introduction of the Euro; real wages became more responsive; labor mobility in the EU-15 is comparable to that in the United States

Bentivogli & Pagano (1999)

Annual net immigration in relation to population, unemployment rate, wage (proxied by real p.c. GDP), time variance of real p.c. GDP (backward looking 3 years moving average) Labor mobility is Higher responsiveness comparable to that in in the United States, the United States for especially no influence the foreign-born of unemployment population, but not for shocks in EA-11 the native population

Article

Beyer & Smets (2015)

Dorn & Zweimüller (2021)

Fahri & Werning (2015)

Research questions

How do labor market responses compare between Europe and the United States? How do labor markets respond to region-specific shocks?

How effective is labor mobility in currency unions with nominal rigidities?

Data

1977–2013, 51 United States and 48 EU regions Blanchard and Katz 1992 model, quasi maximum-likelihood-based approach, vector autoregression approach

What are the patterns of labor mobility in Europe? Has the European labor market become more integrated? What are the obstacles to labor market integration? EEA countries (incl. CH and the UK) Descriptive statistics

Method

Main variables

Dependent: employment level, employment rate, participation rate. Independent: region-specific variables and country variables

Wage differences, share of tertiary-educated individuals, share and origin of foreign residents

No data General equilibrium model with agents, firms, and government; one-time unanticipated shocks Consumption, labor, residence

(continued)

APPENDIX 1: SUMMARY OF LABOR MOBILITY LITERATURE

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(continued) Article

Beyer & Smets (2015)

Dorn & Zweimüller (2021)

Fahri & Werning (2015)

Results

Convergence in the adjustment process, adjustment in Europe twice as long; less important adjustment mechanism in Europe

No common European labor market owing to heterogeneity of languages and cultures, national regulations, and discrimination against immigrants

Labor emigration from depressed regions with welfare gains for movers; only little welfare impact on stayers in cases of internal demand imbalances; improvement in stayers’ welfare in cases of external demand imbalances

Article

Huart & Tchakpalla (2019)

Krause et al. (2017)

Research What are the benefits of quesincreased labor mobility tions and how do they compare to fully flexible exchange rates?

What are the responses of labor mobility in the EA to national labor market conditions?

Data

Panel dataset for 14 EA countries for 1999–2015

What do experts think about the current state of European labor market mobility, and how can it increase? Online survey among European labor market experts (IZA research and policy fellows)

Method

House et al. (2018)

United States (48 states) 1977–2015, Canada (10 provinces) 1977–2015, Europe (12 core EA countries incl. Denmark, whose currency is pegged, + 17 European countries) 1995–2015 Multi-country DSGE model with cross-border migration, search frictions, and country-specific shocks (demand for local goods) but no TFP shocks; two counterfactual simulations

OLS regression with country and time fixed effects

Online survey

(continued)

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(continued) Article

House et al. (2018)

Main variables

Gross and net migration, unemployment rates

Results

Huart & Tchakpalla (2019)

Krause et al. (2017)

Dependent: mobility with the world, mobility with the EU, mobility with the EA, stocks of foreign population. Independent: lagged unemployment differentials, lagged wage differentials, relative GDP p.c. growth Migration responds to Mobility in the EA is unemployment differentials. relatively low, responding Labor mobility and flexible to national differences in exchange rates both work unemployment, but neither to reduce unemployment to wage nor GDP p.c. and p.c.a. GDP differences differentials

Not applicable

Labor market mobility has not been achieved yet. Recognition of professional qualifications, harmonized social security systems, and knowledge of several languages are necessary

Article

Modestino & Dennett (2018)

Puhani (2001)

Puiu (2011)

Research questions

Does negative home equity decrease state-to-state mobility in the United States?

Can labor mobility act as a sufficient adjustment mechanism in the case of asymmetric shocks in the EA?

Data

State-to-state migration data 2006–2009, United States Logistic regression Migration, share of underwater nonprime households

Regional panel data for Western Germany, France, and Italy Regression Net immigration (population growth), unemployment rates, and income (GDP)

Can labor mobility act as a sufficient adjustment mechanism in the case of asymmetric shocks in the EA? 2010, 17 EA members

Method Main variables

Stylized facts Net migration rate, labor market freedom (index based on minimum wages, hiring & firing regulation, and centralized wage bargaining) (continued)

APPENDIX 1: SUMMARY OF LABOR MOBILITY LITERATURE

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(continued) Article

Modestino & Dennett (2018)

Puhani (2001)

Puiu (2011)

Results

Reduction in national state-to-state migration by 0.05 percentage points by negative home equity, negligible impact on national unemployment rate

It is unlikely that labor mobility can serve its role as a sufficient adjustment mechanism even within nation-states in the case of asymmetric shocks in the EA as the accommodation of a shock to unemployment takes several years

Labor mobility does not act as a sufficient adjustment mechanism in the case of asymmetric shocks in the EA

Article

Decressin and Fatás (1995) Obstfeld and Peri (1998)

Dao (2014)

Research questions

How do labor market dynamics compare in the European Union and the United States?

How has the pattern of labor market adjustment mechanism evolved in the United States in response to regional and aggregate shocks?

Data

United States (51 states and D.C.), European Union (11 countries with 51 regions), different time frames from 1968 to 1987 Bivariate time series regressions and VAR approach based on the Blanchard & Katz model (1992)

How are idiosyncratic shocks absorbed in the United States, Canada, and three European countries? United States, Canada, Germany, Italy, UK

Bivariate time series regressions and VAR approach based on the Blanchard & Katz model (1992) Employment, unemployment, participation rate, regional labor demand shocks, inflation, net federal transfers

VAR approach based on the Blanchard & Katz model (1992)

Method

Main variables

Employment, unemployment, participation rate

United States

Employment, unemployment, participation

(continued)

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(continued) Article

Decressin and Fatás (1995) Obstfeld and Peri (1998)

Dao (2014)

Results

More common employment trends in the United States than in the EU; immediate effect of regional shocks on migration in the United States in contrast to the short-run absorption of regional shocks mainly by the participation rate in the EU

The role of interstate migration to regional labor demand shocks decreased, whereas unemployment and participation increased in their importance. During aggregate downturns the response of interstate migration was stronger, especially during the Great Recession

Larger role of interregional transfers outside the United States, especially in the European countries

Appendix 2: Summary of Capital Mobility Literature

See Table A.2. Table A.2Summary of the literature on capital mobility Article

Abiad et al. (2009)

Canale et al. (2018)

Drakos et al. (2018)

Research questions

What determines current account balances? Is financial integration responsible for income convergence?

What is the correlation between national saving and investment?

Data

Five-year non-overlapping observations 1975–2004 (total: 6) OLS regressions

What are the foundations of the trilemma (trade-off between free capital mobility, financial stability & fiscal policy flexibility) faced by EA member countries? 11 EA members, quarterly data 1999–2012 Description of the three indices in the panel and time dimensions; linear regression with weighted indices that add up to 1

Maximum-likelihood-based panel cointegration (Johansen multivariate cointegration and Pedroni residual-based panel cointegration tests)

Method

Annual data from 1970 to 2015 on 14 EU countries

(continued)

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kabderian Dreyer and P. A. Schmid, Optimal Currency Areas and the Euro, Volume II, https://doi.org/10.1007/978-3-031-38867-5

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(continued) Article

Abiad et al. (2009)

Canale et al. (2018)

Drakos et al. (2018)

Main variables

Dependent variable: current account to GDP ratio. Independent variables: log p.c. GDP, growth p.c. GDP, fiscal balance ratio, etc No uphill flows of capital in Europe; income convergence due to downhill flows made possible by financial integration

Construction of three indices: free capital mobility index, financial stability index & fiscal policy flexibility index

Dependent: investment ratio. Independent: lagged investment ration, lagged savings ratio & various controls

There is a trilemma that argues for centralized financial supervision

Moderate degree of capital mobility

Results

Article

Globan (2014)

Kondeas (2014)

Research questions

Can capital mobility be measured by the intensity of reaction of capital flows to shocks in domestic and EA interest rates in European post-transition economies? Eight European post-transition economies

What is the role of capital mobility in the creation of asset bubbles and financial crises?

Data

Method

Main variables Results

Development of a new empirical measure of capital mobility, vector autoregressions Net capital flows, benchmark EA interest rate, domestic interest rate, exchange rate Increase in the explanatory power of interest rates for the movement of capital flows shortly before and after the accession to the EU

1980s’ Latin American crisis, 1990s’ East Asian crisis, 2009/ 10 EA crisis Description

Arbitrary variables suitable for the respective crisis Free cash flows can cause asset bubbles in small or developing economies, which are prone to burst when the capital flows are eventually reversed

Appendix 3: Data

Data Used for Both Bilateral and Aggregate Studies Population We collected population time series for each country popi,t from Eurostat. Table reference code is [demo_pjan] “Population on 1 January by age and sex.” Unemployment The data source for total unemployment for each different country as a percentage of the total labor force u i,t is the World Bank. All series were collected using the Eikon database. In Eikon, the codes for these series are those ending in WDA425R, beginning with the two-letter prefix of each country. We calculated simple averages of unemployment rates of the individual countries to reach what we called EU unemployment u EU,t . In our regressions, we use the relationship between unemployment rates of u , or unemployment rates in specific countries in the bilateral studies u i,t j,t specific countries compared to the EU average unemployment rates in u i,t the aggregate studies u EU,t .

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kabderian Dreyer and P. A. Schmid, Optimal Currency Areas and the Euro, Volume II, https://doi.org/10.1007/978-3-031-38867-5

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USD Real GDP Per Capita The data source for the GDP of each different country at constant 2010 USD prices is the World Bank. All series for the different countries were collected using the Eikon database. In Eikon, the codes for these series are those ending in WD20NLC, beginning with the two-letter prefix of each country. Next, we divided the GDP by the population of each country to find the figures for USD GDP per capita for each individual country’s G D P i,t . For the relative variables of our regressions, we either compared GDP the per capita USD GDP between two countries G D P i,t in the bilateral j,t studies, or the per capita USD GDP of a specific country compared to G D P i,t in the aggregate studies. the EU average G D P EU,t USD Nominal GDP For the GDP in current USD prices, we use figures from the World Bank. All series for the different countries were collected using the Eikon database. In Eikon, the codes for these series are those ending in WD2OO1A, beginning with the two-letter prefix of each country. This figure was used to calculate capital flows (change in claims and liabilities) in relative sizes for each country’s GDP. Real GDP Per Capita in Local Currency The data source for the GDP in the local currency of each different country at constant prices is the World Bank. All series for the different countries were collected using the Eikon database. In Eikon, the codes for these series are those ending in WDALAPC, beginning with the two-letter prefix of each country. Then, we divided the GDP by the population of each country to find the figures for per capita real GDP in local currency for each individual country. We calculated the growth of real GDP per capita in local currency for each country and used this figure for our graphical analysis of GDP growth. Social Spending The data source for social spending as a percentage of each country’s GDP SocSpei,t /G D P i,t is the International Monetary Fund (Government Finance Statistics database). All series for the different countries

APPENDIX 3: DATA

115

were collected using the Eikon database. In Eikon, the codes for these series are those ending in GFF0XO, beginning with the two-letter prefix of each country. For the relative variables of our regressions, we either SocSpe /G D P compared the social spending of two countries SocSpe i,t /G D P i,tj,t in the j,t bilateral studies, or the social spending of a specific country compared SocSpe /G D P to the EU average SocSpe i,t /G D P i,t in the aggregate studies. EU,t EU,t

Time Trend To measure time trend in our regressions, we simply added the time period in years associated with each observation Y ear t . Distance The distance between capitals of countries is available at www.mapcrow. info/. We used these differences between capitals as input for our distance series Dist i, j .

Data Used for Aggregate Studies Immigration and Emigration For aggregate data on immigration and emigration of European countries, our source is the Eurostat. For total and intra-EU immigration and emigration, we use the data sources “Immigration by age group, sex and country of previous residence [migr_imm5prv]” and “Emigration by age group, sex and country of next usual residence [migr_emi3nxt].” We divide both immigration and emigration figures by the population of each country in order to find numbers that are proportional to each country’s population. Then, we calculate the differences and sums of the results N et Mig V ol Mig to reach net migration pop i,t and total migration volume pop i,t , i,t i,t respectively (all relative to population size). EU and EA Memberships The dummy variables representing EU and EA memberships of a country in time (EU i,t , E Ai,t ) are generated manually. If in a year a country was a member, we associated the value of 1 to this country, zero otherwise.

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Data Used for Bilateral Studies Immigration and Emigration Although not optimal, we collected data on bilateral immigration and emigration from two sources: Eurostat (data reference: migr_imm5prv and migr_emi3nxt) and the International Migration Institute (data reference: dmig_c2c). Often, they provide complementary data to each other. However, even though we checked that both sources reference the same country sources for their data files, sometimes they contradict each other. The DMIG data is discontinued after 2015. Thus, from 2016 to 2020, we only use data from Eurostat. For data prior to 2015, we manually compiled the data from both sources into a single file, checking the data provided with the original country sources. We divide bilateral immigration and emigration by population size of country i to achieve migration figures that are proportional to each country’s population in percentages. Next, we calculate net bilateral N et Mig i, j,t V ol Mig and total volume of bilateral migration pop i, j,t immigration popi,t i,t by taking the simple difference and summing up bilateral immigration and emigration percentages, respectively. Capital Liabilities and Claims The data source for bilateral capital flows is the Bank for International Settlements (BIS) (Ref: Locational Banking Statistics). From this data, we selected the total change in claims and liabilities in USD of each country i in another country j. We calculate these figures in relation to nominal USD GDP over time, which allows us to calculate the net change in N et Del Lia i, j,t and the total a country’s i liabilities relative to country j G D PU S i,t V ol DelCap

intensity of capital movement between two countries G D PU Si,ti, j,t . Both variables are expressed as a percentage of each country’s i GDP. EU and EA Memberships In the bilateral studies, we considered whether each pair of countries were members of the EU and the EA. Thus, the variables EU i, j,t and E Ai, j,t received the value of 1 if both countries in a pair were members, and 0 otherwise.

Appendix 4: Cook’s Distance

For ordinary least-squares regressions, the Cook’s distance (Cook, 1977) is a convenient way to check how influential a point is in determining estimation results. In our case, we decided to use Cook’s distance to check for validity in our econometrical results, possibly pointing out in a systematic way which points could be considered outliers. The equation that allows us to calculate the Cook distance for each regression point is:   ri2 h ii (A.2.1) MSE Di = p (1 − h ii )2 where ri is the residual of point i, p represents the number of estimates in the regression, MSE is the mean squared error, and h ii is the “leverage value” associated with point i. Cook’s distance measures to what degree the fitted values of a regression would change if the particular point i were removed. This means that a large value for D can be interpreted as evidence that the associated point D significantly changes the fitted values of the regression model. To view this, we can write Eq. A.2.1 thus: n Di =

j=1







y i − y j (i )

2

pM S E

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kabderian Dreyer and P. A. Schmid, Optimal Currency Areas and the Euro, Volume II, https://doi.org/10.1007/978-3-031-38867-5

(A.2.2)

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where y j(i ) is the estimated y when removing point i. As a rule of thumb, often the value of D = 4/n (where n is the number of data points in a regression) is used to set a cutting bar to indicate outliers.

Reference

Cook, R. D. (1977). Detection of influential observations in linear regression. Technometrics, 19(1), 15–18.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kabderian Dreyer and P. A. Schmid, Optimal Currency Areas and the Euro, Volume II, https://doi.org/10.1007/978-3-031-38867-5

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Index

A Adjustment mechanism, 2, 3, 10, 11, 23–25, 30, 107–109 Adverse labor demand shock, 17 Asymmetric shock, 1, 2, 23, 26–28, 30, 32, 34, 40, 51, 61, 63, 82, 84, 105, 108, 109 B Bank-oriented financing systems, 29 Bilateral relations, 55, 61, 82, 84 Bilingualism, 14 BK model, 25, 26 Boom-bust cycle, 4, 31, 34, 84, 85 Business cycle, 1, 2, 9, 84, 101 C Capital Markets Union, 28 Capital mobility, 3–6, 28–33, 76, 77, 80, 98, 99, 103, 104, 111, 112 Common market, 14, 19, 25, 28 Cook’s distance, 49, 82, 117

Country-specific shock, 26, 107 Cultural distance, 14, 16 Currency union, 1, 3, 5, 30, 63, 77, 82, 102, 106

D Discrimination, 16, 27, 107 Domestic investment, 3, 30 Domestic savings, 3, 30

E East-West migration, 12, 24 Economic growth, 4, 29 Economic shock, 2, 3, 5, 17, 30, 44, 73, 77, 78, 102 Economic stabilization, 4 Efficiency wages, 16 Employment, 12, 14, 16–18, 23, 106, 109, 110 Endogeneity hypothesis, 25, 26 Endogenous development, 5, 63, 102

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Kabderian Dreyer and P. A. Schmid, Optimal Currency Areas and the Euro, Volume II, https://doi.org/10.1007/978-3-031-38867-5

121

122

INDEX

European Union (EU), 1, 3, 5, 12, 14, 16, 17, 19, 23, 25–29, 32–34, 40–44, 49–59, 61, 63, 73, 76–79, 82, 84, 85, 98, 99, 102, 103, 105, 106, 108–116

F Factor mobility, 1–5, 9, 19, 101 Feldstein–Horioka puzzle, 33 Financial crisis, 4, 31, 33, 34, 84, 112 Financial integration, 3, 29, 34, 111, 112 Financial stability, 32, 33, 103, 111, 112 Fiscal policy flexibility, 33, 111, 112 Fixed exchange rates, 2 Flexible exchange rates, 2, 3, 107, 108

H Home bias, 5, 29, 98, 99

I Idiosyncratic shock, 2, 109 Integrated common market, 25 International risk sharing, 31 Interregional transfers, 24, 110 Interstate migration, 25, 26, 110 Investment, 5, 30–33, 77, 99, 111, 112

L Labor force participation, 12, 14, 23 Labor mobility, 2–5, 12, 14, 23–28, 32, 39, 43, 78–80, 82, 99, 102, 103, 105–109

Labor supply, 3, 10, 12, 17, 18, 23, 24, 27 Language barrier, 14, 26

M Market-oriented financing systems, 29 Mass migration, 12 Migration, 2–5, 11, 12, 19, 23–28, 40–44, 49, 51, 53–64, 66, 68, 70, 72, 73, 76, 78, 102, 103, 107–110, 115, 116

N Net capital mobility, 82 Net migration, 24–26, 40, 41, 49–53, 73, 105, 108, 115 Nominal wages, 2, 19, 21

O Optimal currency areas (OCA), 1, 2, 5, 6, 9, 23, 32, 101

R Real wages, 2, 3, 10–12, 19, 102, 106 Redistribution, 5, 51, 61 Regional economy, 2

S Savings, 3, 30, 32, 33, 98, 103, 111, 112 Social benefits, 4, 51, 73, 85 Social security system, 24–26, 108 Sovereign debt crisis, 27 Stabilization mechanism, 4 Synchronization, 1, 2, 9, 101

INDEX

T Total flows, 27, 28, 53, 61, 76 Trilemma, 33, 111, 112

123

U Unemployment, 2–5, 12, 14, 17–19, 23–28, 34, 40, 41, 43, 44, 49, 51, 53, 55, 61, 63, 73, 76–78, 82, 84, 85, 98, 102, 103, 106, 108–110, 113