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Youth Labor in TransitionInequalities, Mobility, and Policies in Europe$

Jacqueline O'Reilly, Janine Leschke, Renate Ortlieb, Martin Seeleib-Kaiser, and Paola Villa

Print publication date: 2018

Print ISBN-13: 9780190864798

Published to Oxford Scholarship Online: January 2019

DOI: 10.1093/oso/9780190864798.001.0001

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What are the employment prospects for young Estonian and Slovak return migrants?

What are the employment prospects for young Estonian and Slovak return migrants?

Chapter:
(p.461) 16 What are the employment prospects for young Estonian and Slovak return migrants?
Source:
Youth Labor in Transition
Author(s):

Jaan Masso

Lucia Mýtna Kureková

Maryna Tverdostup

Zuzana Žilincˇíková

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190864798.003.0016

Abstract and Keywords

This chapter addresses patterns of return migration in Estonia and Slovakia. It investigates the selection of emigrants who decide to return home and analyzes their characteristics compared to emigrants who remain abroad and to fellow nationals who did not emigrate, as well as the labor market status of young returnees after re-entering the domestic labor market. The comparative analysis of the two national Labor Force Survey samples suggests that among young returnees, level of education has no association with the decision to return home. An education–occupation mismatch affects the decision to return among young and highly educated Estonian migrants, whereas no such effect is found for young Slovak returnees. The analysis of post-return labor market status reveals that both Estonian and Slovak returnees are more likely to face short-term unemployment after re-entering the domestic labor market than are emigrants who remain abroad or people who stayed at home.

Keywords:   return migration, Estonia, Slovakia, labor market, integration, unemployment

16.1. Introduction

Free mobility is an important aspect of European integration that was widely realized for those Central and Eastern European (CEE) countries that joined the European Union (EU) in 2004 and 2007. Many young and highly educated people from these countries have since sought employment in Western Europe (Kahanec and Zimmermann 2010). The key findings about East–West migration refer to the selection of emigrants on the basis of age and level of education, to emigrants’ employment in low-skilled and low-paid jobs, and to their relatively weak upward occupational mobility (Drinkwater, Eade, and Garapich 2009; Kahanec and Zimmermann 2010; Voitchovsky 2014). The quality of the employment of CEE migrants in the West is significantly worse than that of young migrants originating from Western countries (Akgüç and Beblavý, this volume; Spreckelsen, Leschke, and Seeleib-Kaiser, this volume). At the same time, CEE migrants in the West have very high employment levels (Kahanec and Zimmermann 2010; Kahanec and Kureková 2013), which even during the economic crisis exceeded the employment levels of nationals in some host countries (Kahanec and Kureková 2016). To date, researchers have mainly focused on understanding the impact of East–West mobility on the receiving countries (Barrett and Duffy 2008; Clark and Drinkwater 2008; House of Lords 2008; Pollard, (p.462) Latorre, and Sriskandarajah 2008) and on evaluating the effects of the outflows for the sending countries (Rutkowski 2007; Galgóczi, Leschke, and Watt 2009; Pryymachenko and Fregert 2011; Organization for Economic Co-operation and Development (OECD) 2012; Zaiceva 2014).

With the onset of the 2008–2009 economic crisis, many observers anticipated that the CEE migrants would return home. The economic literature mostly refers to return migration as a positive phenomenon for the home country, with returnees being viewed as agents of modernization and development, given that they bring home economic and social capital acquired abroad (King 1978). The existing evidence suggests that return patterns in the EU since the crisis have been diverse across both host and home countries (Galgóczi, Leschke, and Watt 2012). This chapter seeks to enhance our knowledge about return migration patterns in two small CEE economies—Estonia and Slovakia.1 Although some comparative studies have recently analyzed return migration to CEE countries (Barcevičius et al. 2012; Lang et al. 2012; Coniglio and Brzozowski 2016), Estonia and Slovakia, in particular, are rarely selected as case studies, and knowledge about return migration in these countries is patchy. We chose these two countries because of their similar post-accession emigration rates, the variation in the severity of the 2008–2009 economic crisis and in respective labor market conditions, and the differences in their institutional models in terms of welfare-state spending changes (Bohle and Greskovits 2012).

We focus our analysis on young emigrants (15–34 years old) who have returned home to Estonia or Slovakia, calling them “returnees” here. Thus, we define a returnee as a person who emigrated from the home country, worked abroad for a period, and subsequently returned home. A “current emigrant,” by contrast, is a person who emigrated from the home country and has remained abroad. A “stayer” is a person who never left the home country (within our observation period) to work abroad. More exact definitions are provided in Section 16.3. We rely on the Estonian and Slovak Labor Force Surveys (LFS) as our source of data. Although the two data sets involve to some extent different types of variables, they enable us to compare the two countries in a structured way. The LFS is a natural choice of data for the comparative analysis of return migration in Europe (concerning earlier studies, see Zaiceva and Zimmermann 2016) because within both the Estonian and the Slovak data, the variable of workplace location—home country or abroad—can be used to identify returnees.

The chapter conducts the analysis in two areas. First, we investigate what might lie behind the decision of some emigrants to return home (selection of returnees), and we seek to identify specific characteristics of returnees relative to those who remained at home (stayers) and those who remained abroad (current emigrants). Second, the chapter provides an analysis of the labor market status of young returnees after they have re-entered the domestic labor market. In summary, our research is centered on two questions: (1) Who are returnees compared to both stayers and current emigrants—among both young people (p.463) and older adults? and (2) How successful are returnees in the home-country labor markets in terms of observed labor market status—that is, how often are they employed, unemployed, or inactive?

The value of our contribution lies in the comparative design of the study, which enables us to test the relative importance of some institutional and macroeconomic factors vis-à-vis micro-level characteristics such as education, gender, and labor market status. On the micro level, we pay particular attention to understanding the impact of being occupationally mismatched while abroad on the selection of returnees and on their short-term labor market outcomes. We also measure the effect of macroeconomic factors—gross domestic product (GDP) per capita and unemployment rate—on the returnees’ labor market performance.

Our findings suggest that among young returnees, level of education has no effect on the decision to return in either of the country-specific samples. At the same time, level of occupation has a significant effect on the selection of young returnees, but only in the Estonian sample. In fact, an education–occupation mismatch significantly affects the decision to return among young and highly educated Estonian emigrants. By contrast, no mismatch effect is found for young Slovak returnees. The analysis of post-return labor market status reveals that both Estonian and Slovak returnees are more likely to face short-term unemployment (after re-entering the domestic labor market) compared to either current emigrants or stayers. This result could be attributed to a higher reservation wage and longer job search periods, both of which returnees can probably afford due to savings accumulated while abroad and possibly also the opportunity to transfer unemployment benefits from the host country to the home country (Hazans 2008; Zaiceva and Zimmermann 2016). These advantages appear to create conditions that enable returnees to find jobs that match their qualification levels and preferences (e.g., wage and type of work). We also find that Estonian returnees have a lower risk of unemployment compared to Slovak returnees. We attribute this difference to better labor market conditions and a broader response of the Estonian social security system to the crisis, both of which facilitate smoother reintegration of returnees in Estonia.

16.2. Literature review: Macro- and micro-level determinants of return migration

On a theoretical level, it has been established that economic actors self-select into migration (Borjas 1987) and that emigrants differ from stayers in terms of both observable (e.g., age, family status, and labor market status) and unobservable (e.g., attitudes and risk aversion) characteristics. The type of selection and how it compares to stayers or to citizens of the host country depends on the home- and host-country characteristics. Similar factors affect the selection (p.464) of returnees. This is most widely analyzed with respect to selection according to skill and ability, as anchored in the theoretical framework of the Roy model (Roy 1951). This model predicts that where migration flows are negatively selected on the basis of skills (i.e., those who emigrate have lower than average skills), return migrants are the best of this negative selection. On the other hand, where the original migrants were positively selected (i.e., those who emigrate have higher than average skills), the return migrants are “the worst of the best” (Borjas and Bratsberg 1996). The aspect of selectivity is important because it signals the characteristics of returnees relative to stayers and is likely to affect returnee behavior in the home labor market, not least via their competitiveness with stayers.

However, the Roy model of selection into return migration overlooks the issue of occupational mismatch, whereas CEE migrants are often mismatched in the host countries, working in jobs below their qualifications (Akgüç and Beblavý, this volume; Spreckelsen et al., this volume). For example, Voitchovsky (2014) argues that the severity of the occupational downgrading of CEE migrants and the related wage penalty stand out relative to those of other migrant groups in Ireland (and the United Kingdom), including third-country nationals. The mismatch is strongest for workers with higher secondary and tertiary education (Drinkwater et al. 2009; Turner 2010). There is some evidence supporting a link between mismatch and return decisions. For instance, overeducation of migrants has been identified as a key variable associated with the intention to return for Estonian migrants working in Finland (Pungas et al. 2012). Similarly, Currie (2007) found that Polish returnees commonly framed their decision to return to Poland within a context of frustration with limited labor market progress in the United Kingdom.

Scholars theorize different reasons for return migration. It may follow, for example, from an initial plan regarding the country of residence over the life cycle, where the return home is already envisaged at the moment of emigration. In an analysis of determinants of return among Moroccan emigrants, for instance, De Haas, Fokkema, and Fassi Fihri (2015) showed that the decision to return can be driven by economic success in the host country. However, the return may also result from mistakes in the initial migration decision; that is, it follows from an unsuccessful migration experience (failed migration) (Rooth and Saarela 2007). The individual and collective success of the return process may vary depending on the individual characteristics of the migrant and his or her household, networks, and community, as well as country-level features in the home and host states (Kveder 2013). Furthermore, precautionary savings may be related to the return decision (Dustmann 1997; McCormick and Wahba 2001). Along these lines, Dumont and Spielvogel (2008, 178) define the key reasons for return migration as a failure to integrate in the host country, changes in the economic situation in the home country (macroeconomic environment), personal preference for the home country, the achievement of a savings objective, (p.465) or improved employment opportunities at home following experience gained abroad.

The variety of factors that can contribute to the success of a return (individual-level characteristics, networks, country-level factors, motive for return, migration experience, and timing of return) is reflected in the mixed empirical findings on the characteristics of returnees and especially on their post-return labor market trajectories and performance across different CEE countries and over time (Iara 2006; Hazans 2008; Martin and Radu 2012; Pungas et al. 2012; Zaiceva and Zimmermann 2016). Coniglio and Brzozowski (2016) document that skill mismatch in the host country is significantly associated with post-return nonconformance of skills and employment, which ultimately reduces the likelihood of successful reintegration.

The majority of studies found that returnees to CEE countries are positively selected in terms of education (Hazans and Philips 2010; Martin and Radu 2012; Smoliner, Förschner, and Nova 2012; Masso, Eamets, and Mõtsmees 2014; Zaiceva and Zimmermann 2016). This positive selection into return migration is reflected in the significant wage premiums of CEE returnees (Iara 2006; Ambrosini et al. 2011; Martin and Radu 2012). However, evidence found by De Coulon and Piracha (2005) indicates that Albanian emigrants are negatively selected on skills, relative to stayers, which to a large extent explains the relatively worse performance of Albanian returnees on the home labor market. Another strand of literature has documented that returnees have a higher probability of falling into unemployment or inactivity (Smoliner et al. 2012; Coniglio and Brzozowski 2016). However, Piracha and Vadean (2010) found that the association between return migration to Albania and unemployment vanishes after a 1-year period of reintegration.

In addition to individual-level factors, institutional and macroeconomic aspects also play a role. Friberg et al. (2014) found that the performance of immigrants to a great extent depends on their structural position in the host labor market, which is largely determined by the institutional configuration of the host-country labor market. Other evidence by Findlay and McCollum (2013) highlights the significance of recruitment and employment regimes in the context of rural agricultural migrant labor. Napierała and Fiałkowska (2013) emphasize the importance of host-country employment agencies in preventing skill–occupation mismatch and, hence, in reducing return migration driven by overqualification. The macroeconomic environment is framed by changing external conditions, such as the Great Recession of 2008–2009, which significantly affected several host and home countries. White (2014), analyzing the return migration of young Polish migrants from the United Kingdom and Ireland following the crisis, questions the strength of a causal effect of the crisis on their decision to return. She argues that migrants prefer to stay in the host country because of the persistence of significant wage differentials compared to Poland. The existing evidence suggests that patterns (p.466) of return in response to the economic crisis have been diverse across both host and home countries (Galgóczi et al. 2012).

To date, systematic work exploring the impact of welfare policies on patterns of return is absent. As stated previously, some studies view returning emigrants as being selected on the basis of a lack of economic success in the host country; return migration would thus correct for the failure of the initial migration. Being unemployed in the host country, therefore, significantly increases the probability of returning to the homeland (Pungas et al. 2012; Bijwaard, Schluter, and Wahba 2014). This might not be quite the case in the context of intra-EU mobility because migrants with a sufficient employment record become eligible for social insurance and other types of welfare benefits in the host country (Kureková 2013). Moreover, under EU legislation, unemployment benefits can be transferred to the country of origin.2 However, if access to welfare is employment based, it continues to exclude the least successful migrants. The few existing studies have noted that choosing to stay or to return home can be influenced by where (at home or in the host country) the emigrant has access to social security benefits (for a discussion regarding Poland during the economic crisis, see Anacka and Fihel 2012) and that the decision of returnees to register as unemployed can depend on the country of previous employment (Kahanec and Kureková 2016). Other findings indicate that unemployment benefits enable emigrants to survive a period of unemployment abroad (White 2014) and that public programs might be important for the successful integration of poorly prepared return migrants (Cassarino 2004).

The contextual factors of the home and host countries go beyond economic and institutional variables. Some studies argue that return decisions are influenced mainly by the home countries rather than the host countries (Martin and Radu 2012) or that private and social motives play a key role (Barcevičius et al. 2012; Lang et al. 2012). Furthermore, cultural factors might be behind a return due to failed migration, such as an inability to integrate in the host country because of prejudices and stereotypes encountered abroad (Cerase 1974), whereas changed cultural and social patterns in the country of origin may also pose challenges to successful reintegration on return (Dumon 1986). Cross-border social network theory emphasizes that cross-border networks of social and economic relationships secure and sustain return migration (Cassarino 2004). For instance, having lost networks of social relationships may be the factor that causes returnees to fail to pursue their interests in the home country. Networks provide access to resources influencing performance on return, whereas return migration may help establish and maintain networks spanning several societies (Cassarino 2004). As an example of the importance of social factors for successful reintegration, Barrett and Mosca (2013) highlight the high degrees of loneliness and social isolation among elderly Irish returnees who had spent long periods abroad compared to those who had stayed at home. However, Kureková and Žilinčíková (2018), using web-survey data for Slovak returnees, find that returning for family (p.467) reasons adds to the success of reintegration. Given this existing body of evidence, understanding the consequences for returning youth emigrants to Estonia and Slovakia can provide a novel and pertinent lens for examining some of the effects of youth migration during the recent crisis period.

16.3. Data

The EU Labor Force Survey (EU-LFS) is a random representative household survey collected on a quarterly basis. The data set is restricted to individuals who are at least 15 years old, and we added an upper limit of 64 years for our study. The EU-LFS employs a rotational panel design, whereby every individual is interviewed for five consecutive quarters of the survey and subsequently leaves the sample. We use this panel structure of the data set to identify those who have work experience abroad. Within both the Estonian and the Slovak data, the variable of workplace location—home country or abroad—is used to identify returnees. The variable of country of residence a year previously, used by other return migration studies (Zaiceva and Zimmerman 2016), does not provide a sufficient sample size in the case of Estonian and Slovak data. A disadvantage of our approach is that we cannot use the data set to observe longer integration patterns and can only comparatively assess labor market outcomes for one quarter (the last quarter of the survey). However, we are able to go beyond the descriptive approach prevalent in most other studies that use EU-LFS data (Martin and Radu 2012; Smoliner et al. 2012).

For the analysis of the Slovak data, we keep only individuals who were interviewed in at least two out of the five available quarters in the sample. We define returnee as a person who worked at least one quarter abroad but returned to Slovakia in the last observed quarter. A current emigrant is an individual who is working abroad in the last observed quarter. In the Estonian LFS data, the labor market history of individuals is available for the past 2 years. Therefore, we define Estonian returnees as those who have worked abroad for at least one quarter during the past 2 years and are back in Estonia in the last quarter. This longer time span for observing emigrants and returnees yields a much larger sample of returnees in Estonia than in Slovakia.

A general disadvantage of the EU-LFS is the fact that it only captures emigration and return migration of short-term emigrants and returnees. A condition of participation in the survey is that an individual is considered a member of a surveyed household; therefore, the survey does not cover emigration of economically independent units (e.g., young people who emigrated and live abroad and are considered economically independent by the household members). However, individuals engaging in temporary or seasonal work abroad (or commuters) are considered household members, even if they work abroad for more than a year, and are therefore included in the survey (Bahna 2013). An important implication (p.468) of this survey design is that the EU-LFS more precisely captures emigrants who live with a broader family and engage in circular or temporary mobility and at the same time is likely to underestimate the mobility of young people who have not established a family and are more footloose. We interpret our results in the light of these limitations.

16.4. Key facts about Estonia and Slovakia

Estonia and Slovakia are understudied countries in the return migration literature. We selected these cases because they experienced similar post-accession emigration rates (Kureková 2011) but showed differences in the severity of the 2008–2009 economic crisis, as well as varying today in their labor market conditions and in their institutional models in terms of changes in welfare spending. The key comparative data for the two countries are presented in Table 16.1.

Table 16.1 Key economic indicators: Estonia and Slovakia

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

Unemployment rate

EU25

8.8

8.5

8.8

9.1

9.2

9.1

8.2

7.2

7.1

9.1

9.7

9.7

10.5

10.9

EE

14.6

13.0

11.2

10.3

10.1

8.0

5.9

4.6

5.5

13.5

16.7

12.3

10.0

8.6

SK

18.9

19.5

18.8

17.7

18.4

16.4

13.5

11.2

9.6

12.1

14.5

13.7

14.0

14.2

Youth unemployment rate (age 15–24 years)

EU25

17.3

16.9

17.4

18.4

18.8

18.7

17.3

15.5

15.7

20.1

21.0

21.2

22.8

23.2

EE

23.9

22.2

17.9

20.9

23.9

15.1

12.1

10.1

12.0

27.4

32.9

22.4

20.9

18.7

SK

37.3

39.6

38.1

33.8

33.4

30.4

27.0

20.6

19.3

27.6

33.9

33.7

34.0

33.7

GDP growth

EU27

3.9

2.0

1.3

1.5

2.6

2.2

3.4

3.2

0.4

-4.5

2.0

1.7

−0.4

0.1

EE

9.7

6.3

6.6

7.8

6.3

8.9

10.1

7.5

-4.2

-14.1

2.6

9.6

3.9

0.8

SK

1.4

3.5

4.6

4.8

5.1

6.7

8.3

10.5

5.8

-4.9

4.4

3.0

1.8

0.9

Social protection expenditures (% GDP)

EU25

25.6

25.7

26.0

26.5

26.3

26.4

26.0

25.5

26.2

28.9

28.7

28.4

28.8

EE

13.8

13.0

12.7

12.6

13.0

12.5

12.0

12.0

14.7

18.8

17.6

15.6

15.0

14.8

SK

19.1

18.7

18.8

18.0

16.9

16.2

16.0

15.7

15.7

18.5

18.3

17.9

18.1

18.4

Strictness of employment protection—individual and collective dismissals: regular contracts

EE

2.33

2.33

2.07

2.07

2.07

2.07

SK

2.63

2.63

2.63

2.63

2.16

2.26

Strictness of employment protection—temporary contracts

EE

2.29

2.29

2.29

2.29

2.29

3.04

SK

2.17

2.17

2.17

2.42

2.29

2.42

EE, Estonia; SK, Slovakia.

Sources: OECD (employment protection) and Eurostat (all other data series).

Slovakia and Estonia have had very different experiences of the economic crisis. They entered the crisis with different levels of youth unemployment, converging by 2010 on very high rates—from which Estonia recovered more quickly than Slovakia, however. Estonian youth unemployment rates skyrocketed from approximately 10% in 2007 to 34% in 2010 and then declined to approximately 19% in 2013. In contrast, the youth unemployment rate in Slovakia was nearly double that of Estonia at the onset of the crisis: It was 19% in 2008 and increased to 34% by 2012, remaining at this level in 2013.

Estonia experienced significant declines in GDP in 2008 and 2009 of 5.4% and 14.7%, respectively. Subsequently, economic growth returned, contributing to a decline in the general unemployment rate from 16.7% in 2010 to 8.6% in 2013. Although Slovakia experienced only a mild GDP decline in 2009 (−4.9%), its success in fighting unemployment has been limited. From this perspective, we might expect that the integration of return migrants to the Estonian labor market would be smoother than that of Slovak returnees.

Moreover, social protection spending has increased considerably in Estonia. Whereas in the mid-2000s, Estonia had a lower level of social protection spending than that of Slovakia (12.4% vs. 15.9%, respectively, in 2005), the levels converged at the peak of the crisis in 2009, with social protection spending amounting to 18.8% versus 18.2% of GDP in Estonia and Slovakia, respectively. This change indicates that Estonia invested significantly in assisting its citizens with weathering the misfortunes of the economic crisis. This increased investment in welfare may have assisted return migrants, but it also discouraged further outmigration from Estonia (Kureková 2013). Which country was more successful in integrating returnees is an important question. Based on these aggregate indicators, we might expect that returnees to Estonia on average perform better at reintegrating into the labor market because of higher levels of labor market flexibility (Eamets et al. 2015), contributing to higher outflows from (p.469) (p.470) unemployment and better labor market conditions. In fact, Estonian returnees show lower unemployment rates than has been the case for returnees to Slovakia.

For the analysis of return migration, we work with a pooled sample of EU-LFS data from 2008 to 2013. The overall Slovak sample consists of 96,821 individuals, of whom 3,211 are current emigrants and 329 are returnees. The total Estonian sample includes 159,028 respondents, of whom 3,002 are current emigrants and 3,570 are returnees. Of the returnees, 62% of the Slovaks and 65% of the Estonians are young (aged 15–34 years).

The rate of return migration increased over time in both countries, but the growth has been especially significant in Estonia (Tables 16.2 and 16.3). By 2013, the rate of return had exceeded the rate of outmigration, resulting in positive net intra-EU mobility in Estonia. The rate of return to Slovakia has been more modest. Between 2008 and 2013, on average every tenth person who worked abroad returned, but the rate of return varies significantly over the years analyzed, reaching close to 20% in 2009 and 2012 but only approximately 7% in all other years.3 The share of current emigrants out of Slovakia relative to (p.471) returnees to Slovakia exceeds the share of current emigrants out of Estonia relative to returnees to Estonia, especially in the crisis years 2008 and 2009.

Table 16.2 Estonia: Numbers of emigrants, returnees, and stayers (full sample)

2008

2009

2010

2011

2012

2013

Total

Stayers

17,763

15,526

15,634

16,660

18,556

18,346

152,456

Returnees

275

413

491

608

778

785

3,570

Emigrants

332

307

365

390

507

492

3,002

Total

18,370

16,246

16,490

17,658

19,841

19,623

159,028

Share of returnees

1.50

2.54

2.98

3.44

3.92

4.00

2.24

Share of emigrants

1.81

1.89

2.21

2.21

2.56

2.51

1.89

Returnees per emigrants

82.8

134.5

134.5

155.9

153.5

159.6

118.9

Source: EE-LFS; authors’ calculations.

Table 16.3 Slovakia: Numbers of migrants, returnees, and stayers (full sample)

2008

2009

2010

2011

2012

2013

Total

Stayers

14,618

14,402

13,563

14,172

13,550

22,976

93,281

Returnees

69

83

30

30

68

49

329

Emigrants

695

484

437

440

357

798

3,211

Total

15,382

14,969

14,030

14,642

13,975

23,823

96,821

Share of returnees

0.5

0.6

0.2

0.2

0.5

0.2

0.3

Share of emigrants

4.5

3.2

3.1

3.0

2.6

3.4

3.3

Returnees per emigrants

9.9

17.1

6.9

6.8

19.0

6.1

10.2

Source: SK-LFS; authors’ calculations.

The EU-LFS does not include information about the main migrant destination countries for Estonia. Other studies document that Finland was, and remains, the most important destination country for temporary labor mobility among young Estonians (aged 15–35 years); the United Kingdom, Austria, Norway, Sweden, and Russia are also popular destinations. The key migration destinations for Slovaks are the United Kingdom, Czech Republic, Hungary, Italy, Austria, and Germany (Masso et al. 2016).

Tables 16.4 and 16.5 present descriptive statistical evidence for key demographic features of returnees, current emigrants, and stayers and for different age brackets. Estonian return migrants are substantially different from both current emigrants and stayers (see Table 16.4). Return migrants are on average younger than those who stay in Estonia; returnees are also more often male, compared to the relevant age group of stayers. Among young returnees, the share of married individuals is 39%, which is higher relative to that of stayers (31%) but lower relative to that of their peers who are still working abroad (49%). In terms of education, young returnees are more educated (e.g., the share of those with a lower level education is 32%, compared to 41% among stayers of the corresponding age group) and predominantly hold a secondary-level education (54%). The examination of labor market status revealed that approximately 72% of young returnees were employed while abroad; however, after returning, the share of those employed dropped to 52%, along with an increase in the share of unemployed from 12% a year previously to 26% in the current year.4 However, despite the better educational attainments of returnees, they are still more likely to be unemployed than are stayers. Among returnees who found work, their occupational profile was lower compared to that recorded in their last quarter abroad. Consequently, young returnees with high education levels more frequently reported themselves to be overeducated in the last quarter working abroad compared to those who had stayed in Estonia (16% relative to 10% among stayers).

Table 16.4 Estonia: Descriptive statistics based on EE-LFS

Returnees

Stayers

Current emigrants

15–35 years

Youth 15–24 years

Youth 25–35 years

>35 years

15–35 years

Youth 15–24 years

Youth 25–35 years

>35 years

15–35 years

Youth 15–24 years

Youth 25–35 years

>35 years

Sociodemographic characteristics

Average age, years

41

45

39

Gender (male = 1)

71.8

68.8

78.2

61.2

50.5

52.5

53.5

44.5

87.2

79.9

89.9

85.7

Nationality (Estonian = 1)

80

82.3

78.4

66.4

78.2

81.4

73.7

73

79.1

85.8

76.1

77.8

Citizenship (Estonian = 1)

92.3

95.4

90.3

79.7

91.5

94

87.9

84.3

94

97.2

92.6

88.2

Marital status (married = 1)

39

16.7

56.7

77.5

30.8

10.3

63.7

75.4

48.9

20.1

59.8

81.1

Education

Higher

13.9

5.2

21.5

18.9

14.2

5.2

28.5

21.9

11.1

4.7

13.5

8.2

Secondary

53.9

60.5

50.4

59.1

44.7

44.6

49.5

50.5

57.8

66.8

54.4

68.3

Lower

32.2

34.3

28.2

22

41.1

50.3

22

27.6

31.1

28.4

32.2

23.5

Employment

Employed

51.9

38.2

60.4

59.5

49.1

23.1

76.2

58.1

100

100

100

100

Unemployed

25.8

26.9

25.2

15.6

8.8

8.6

9

5

Inactive

22.3

35

14.4

24.9

42.1

68.3

14.9

36.9

Employment 1 year previously

Employed

72

58.2

79.3

77

61.8

35.7

78.5

60.1

79.9

62.2

85.3

78.5

Unemployed

12

11.6

11.2

6.8

3.3

3.4

3.3

3.3

6.9

13.5

4.9

7.2

Inactive

16

30.2

8.5

16.2

34.9

60.9

18.3

36.6

13.2

24.3

9.8

14.3

ISCO (last quarter abroad for returnees)

High

7.7

6.2

8.8

11.3

37.9

22.5

42.8

39.2

8.7

5

10.1

13.1

Medium

7.2

9.9

5.1

8.1

23.1

32.4

20.1

20.2

9.8

16

7.5

7.3

Low

85.1

83.9

86.1

80.5

38

44.8

35.8

40.2

81.2

79

82.4

79.5

Overeducation (last quarter abroad for returnees)

Among medium educated

11.1

12.9

9.6

7.8

8.2

11.2

7

11

11.2

13.5

10.1

8.9

Among highly educated

16.1

36.4

13.2

3.7

10.2

13.8

9.8

10.6

32.6

60

29

18.9

Self-employed (last quarter abroad for returnees)

2.8

4.5

1.5

2.9

6.2

2.6

7.4

9

1.5

0.9

1.7

3.7

No. of observations

1,042

280

701

1,563

29,770

15,189

14,581

106,009

794

219

575

1,424

Notes: The level of occupation corresponds to the International Standard Classification of Occupations (ISCO) code: low (9), medium (4–8), and high (0–3). Overeducation was measured as a combination of education and occupational level. Overeducation among the medium educated was defined as the combination of medium education (ISCED 3 or 4) and low occupational level (ISCO 9); overeducation among the highly educated was defined by high education (ISCED 5 or 6) and a low or middle level of occupation (ISCO > 3).

Table 16.5 Slovakia: Descriptive statistics based on SK-LFS

Returnees

Stayers

Current emigrants

15–34 years

Youth 15–24 years

Youth 25–34 years

>35 years

15–34 years

Youth 15–24 years

Youth 25–34 years

>35 years

15–34 years

Youth 15–24 years

Youth 25–34 years

>35 years

Sociodemographic characteristics

Average age, years

n.a.

n.a.

n.a.

Gender (male = 1)

63.7

50.6

72.4

73.6

51.1

50.9

51.2

46.9

67.6

62.7

69.7

70.5

Nationality (Slovak = 1)

85.3

85.2

85.4

83.2

90.2

90.4

89.9

89.1

88.2

90.7

87.1

87.9

Citizenship (Slovak = 1)

99.5

100

99.2

99.2

99.9

100

99.7

99.8

99.7

100

99.6

99.7

Marital status (married = 1)

13.7

0

22.8

70.4

22.1

2.9

42

75.7

21

3.6

28.4

74.3

Education

Higher

7.4

3.7

9.8

2.4

15

5.8

24.6

13.7

10.9

4.3

13.8

5

Secondary

89.7

91.4

88.6

91.2

57.9

46.9

69.4

75.3

86.1

90

84.4

91.1

Lower

2.9

4.9

1.6

6.4

27

47.4

6

11.1

3

5.7

1.8

3.9

Employment

Employed

33.3

28.4

36.6

32.8

42.8

17.5

68.9

62.3

98

99.1

97.5

98.7

Unemployed

59.3

60.5

58.5

53.6

12

10.6

13.4

9.8

0.1

0

0.2

0

Inactive

7.4

11.1

4.9

13.6

45.3

71.9

17.7

27.9

1.9

0.9

2.3

1.3

Employment 1 year previously

Employed

70.6

61.7

76.4

85.6

39.2

13.6

65.8

63.3

84.9

73.2

89.8

94.2

Student

10.8

23.5

2.4

0.0

40.1

74.2

4.8

0.0

4.8

12.7

1.5

0.0

Unemployed

17.2

14.8

18.7

10.4

11.4

9.1

13.8

10.5

8.8

13.9

6.6

4.7

Inactive

1.5

0

2.4

4.0

9.2

3.1

15.6

26.2

1.6

0.2

2.1

1.2

No. of observations

204

81

123

125

34,582

17,595

16,987

58,698

1,473

440

1,033

1,738

Labor market characteristics

Occupation/ISCO (last quarter abroad for returnees), N = 56,789

High

11

6.2

14.2

8.8

36.6

22.6

40.4

36.2

15

9.1

17.6

7.9

Medium

62.2

66.7

59.2

72

56.1

66.2

53.4

54.3

66

67.1

65.6

79.5

Low

26.9

27.2

26.7

19.2

7.3

11.1

6.2

9.5

19

23.7

16.9

12.7

Overeducation (last quarter abroad for returnees)

Among medium educated

25.9

24.7

26.7

14.4

5.7

8.1

5

7

16.8

20.3

15.2

11.1

Among highly educated

3

1.2

4.2

0.8

3.3

1.8

3.8

1.8

4.2

2.1

5.1

1.7

Self-employed

6.0

2.5

8.3

13.6

12.7

8.6

13.8

15.3

19.2

14.8

21.0

35.7

No. of observations

201

81

120

125

15,248

3,261

11,987

38,031

1,451

438

1,013

1,733

Notes: Overeducation was measured as a combination of education and occupational level. Overeducation among the medium educated was defined as the combination of medium education (ISCED 3 or 4) and low occupational level (ISCO 9); overeducation among the highly educated was defined by high education (ISCED 5 or 6) and a low or middle level of occupation (ISCO > 3).

For Slovakia (see Table 16.5), we find that returnees significantly differ both from stayers (nonmigrants) and from Slovak emigrants currently working abroad with regard to the main demographic and labor market characteristics. Similar to current emigrants, returnees are more likely to be males. Returnees are younger, more frequently overeducated for the jobs they performed abroad, and more skilled than both current emigrants and stayers. Most young returnees have a secondary education (90%); however, approximately two-thirds of returnees were unemployed in the last quarter of the survey, which exceeds the share of unemployed among stayers and especially among Slovak emigrants abroad.

However, returnees are also much less likely to be inactive compared to stayers in the relevant age categories. Returnees are less likely than stayers to be self-employed, which might be related to their better performance in the labor market (e.g., no need to enter bogus self-entrepreneurship; see Ortlieb, Sheehan, and (p.472) (p.473) (p.474) (p.475) (p.476) Masso, this volume). But this may also be associated with the lower frequency of opportunity entrepreneurship (Bosma et al. 2012) among return migrants. These findings contrast with some other previous findings (McCormick and Wahba 2001; Piracha and Vadean 2010); however, the EU-LFS might not be the appropriate data source for studying the degree of self-employment among returnees because they may require more time after their return home to become engaged in entrepreneurship.5 In the empirical analysis that follows, we examine whether these differences are statistically salient and to what degree these compositional effects impact on the labor market performance of returnees relative to stayers and current emigrants.

16.5. Econometric analysis of selectivity and labor market status

16.5.1. Models

The econometric analysis has two foci. First, a set of logistic regressions is used to investigate how the characteristics of returnees differ from those of both stayers and current emigrants. Second, the labor market status of returnees is investigated in comparison to the rest of the respondents—stayers and current emigrants. A multinomial logistic regression is fitted for the variable indicating labor market status in the last observed quarter: employed, unemployed, or inactive. All models are estimated for the full sample (M1–M3), as well as for the youth sample only (M4–M6). Results are shown in Tables 16.6 and 16.7 for Estonia and in Tables 16.8 and 16.9 for Slovakia.

The models include two broad types of variables: individual-level variables and macroeconomic variables. In particular, the models include sociodemographic variables: gender; marital status (single or married); age; nationality (Estonian/Slovak or non-Estonian/non-Slovak); and education—low (International Standard Classification of Education (ISCED) 1–2), medium (ISCED 3–4), and high (ISCED 5–6). The models addressing the selectivity of returnees further employ variables related to the economic activity of respondents: self-employment (a dummy variable), labor market status a year previously (employed [ref.], student, unemployed, or inactive), skill level of job after return, and overqualification while abroad. We distinguish between two types of overqualification: overqualified among medium-educated and overqualified among highly educated workers.

Macro-level characteristics include measures of GDP per capita and unemployment rate in the home country. Based on the findings of secondary literature, host-country conditions appear more important than home-country conditions for the return and reintegration of emigrants. We are not able to use these macro-level variables in the host countries (or their differences in the host and home countries) because of the lack of information on the migrants’ destination (p.477) (p.478) (p.479) (p.480) (p.481) (p.482) (p.483) (p.484) (p.485) (p.486) (p.487) countries in the Estonian data. The variables GDP per capita and unemployment rate are measured at the national level in the home countries, and we use quarterly data for these. We also include year dummies to capture other aggregate-level dynamics. The models are organized in three modifications: baseline models (individual-level variables only), models with year dummies, and models with macroeconomic variables. The models are identical for the two countries.

16.5.2. Results: Estonia

16.5.2.1. Selectivity Analysis

The results of the selectivity analysis for the Estonian sample are presented in Table 16.6. Returnee–stayer and returnee–migrant selections are studied in both the young group (aged 15–34 years) and the total sample. We first focus on the returnee–stayer selection framework. The estimates based on the total sample showed that the likelihood of being a returnee decreases with age; for example, the odds of being a returnee are highest for those aged 15–24 years.6 Because young returnees are of prime interest, we explicitly analyze their selection patterns. We found that young returnees are more likely to be male, relative to stayers (the same holds in the total sample). Returnees aged 15–34 years are more likely to hold a secondary education qualification (models M4 and M6). However, higher education does not significantly affect the decision to return in the sample of young people, whereas in the total sample both secondary and higher education play a role in the selection of returnees. In terms of job-related characteristics, young returnees are less likely to occupy medium-level positions relative to low-level occupations, and they are more likely to have high-level occupations.7 This suggests a bimodal selection of returnees with respect to the skill level of occupation in returnee–stayer selection (i.e., we can observe positive selection from both low- and high-level occupations). Compared to stayers aged 15–34 years, returnees have less likelihood of being self-employed, more likelihood of being unemployed, and are less likely to be inactive 1 year before the interview. It is interesting to note that being overeducated shortly before return significantly disincentivized return among medium-educated youth. At the same time, among highly educated youth (but not in the total sample), a mismatch significantly increased the likelihood of return relative to current emigrants. In terms of macro-level variables, as expected, a higher home-country unemployment rate and GDP level are positively linked to the probability of being a returnee in both the young and the total samples.

Table 16.6 Estonia: Selectivity analysis

Returnee–stayer

Returnee–emigrant

All sample

Youth sample (15–34 years)

All sample

Youth sample (15–34 years)

M1

M2

M3

M4

M5

M6

M7

M8

M9

M10

M11

M12

Male

0.003*

0.003**

0.004**

0.008**

0.008**

0.01***

–0.294***

–0.293***

–0.299***

–0.314***

–0.314***

–0.308***

(0.001)

(0.001)

(0.002)

(0.003)

(0.003)

(0.004)

(0.020)

(0.020)

(0.020)

(0.041)

(0.041)

(0.042)

Married

–0.001

–0.001

–0.001

–0.003

–0.003

–0.003

–0.025

–0.028

–0.026

–0.009

–0.011

–0.011

(0.001)

(0.001)

(0.002)

(0.003)

(0.003)

(0.003)

(0.023)

(0.023)

(0.023)

(0.029)

(0.029)

(0.029)

Age 15–24 years

0.017***

0.02***

0.022***

–0.022

–0.027

–0.029

(0.003)

(0.003)

(0.003)

(0.042)

(0.042)

(0.042)

Age 25–34 years

0.012***

0.013***

0.015***

–0.115***

–0.119***

–0.12***

(0.002)

(0.002)

(0.002)

(0.036)

(0.035)

(0.036)

Age 35–44 years

0.011***

0.012***

0.014***

–0.075**

–0.079**

–0.081**

(0.002)

(0.002)

(0.002)

(0.034)

(0.034)

(0.034)

Age 45–54 years

0.006***

0.007***

0.008***

–0.104***

–0.108***

–0.109***

(0.002)

(0.002)

(0.002)

(0.035)

(0.035)

(0.036)

Other non-Estonian nationality

–0.001

–0.001

–0.001

–0.008***

–0.008**

–0.009**

0.013

0.011

0.015

–0.038

–0.036

–0.029

(0.001)

(0.001)

(0.002)

(0.003)

(0.003)

(0.004)

(0.022)

(0.022)

(0.022)

(0.037)

(0.037)

(0.037)

Secondary education

0.011***

0.008***

0.009***

0.008**

0.005

0.007*

0.03

0.028

0.035

0.036

0.046

0.059*

(0.002)

(0.002)

(0.002)

(0.003)

(0.003)

(0.004)

(0.023)

(0.023)

(0.023)

(0.031)

(0.031)

(0.031)

Higher education

0.017***

0.014***

0.016***

0.008

0.004

0.005

0.145***

0.145***

0.156***

0.012

0.022

0.04

(0.003)

(0.003)

(0.004)

(0.007)

(0.007)

(0.008)

(0.035)

(0.035)

(0.036)

(0.06)

(0.06)

(0.061)

Overeducated among medium educated

–0.018***

–0.019***

–0.022***

–0.059***

–0.06***

–0.07***

–0.196***

–0.199***

–0.193***

–0.564***

–0.556***

–0.554***

(0.004)

(0.004)

(0.004)

(0.022)

(0.022)

(0.025)

(0.053)

(0.054)

(0.054)

(0.192)

(0.193)

(0.190)

Overeducated among highly educated

–0.004

–0.004

–0.005

0.021***

0.019***

0.022***

–0.051

–0.052

–0.057

0.15**

0.155**

0.145*

(0.004)

(0.004)

(0.005)

(0.007)

(0.007)

(0.008)

(0.055)

(0.055)

(0.056)

(0.076)

(0.075)

(0.077)

Medium-level occupation

–0.01***

–0.01***

–0.013***

–0.019***

–0.02***

–0.024***

–0.347***

–0.343***

–0.339***

–0.511***

–0.503***

–0.5***

(0.003)

(0.003)

(0.004)

(0.006)

(0.006)

(0.007)

(0.046)

(0.046)

(0.047)

(0.067)

(0.067)

(0.068)

High-level occupation

0.02***

0.02***

0.023***

0.015***

0.014***

0.016***

–0.181***

–0.177***

–0.182***

–0.321***

–0.316***

–0.317***

(0.003)

(0.003)

(0.003)

(0.005)

(0.005)

(0.006)

(0.024)

(0.024)

(0.024)

(0.033)

(0.034)

(0.034)

Self-employed

–0.015***

–0.015***

–0.017***

–0.013*

–0.012*

–0.014

–0.02

–0.016

–0.012

0.046

0.056

0.049

(0.003)

(0.003)

(0.004)

(0.007)

(0.007)

(0.009)

(0.056)

(0.056)

(0.057)

(0.098)

(0.095)

(0.096)

Labor market status 1 year ago—student

–0.002

–0.002

–0.002

–0.006

–0.006

–0.007

0.022

0.024

0.026

0.023

0.024

0.037

(0.003)

(0.003)

(0.003)

(0.004)

(0.004)

(0.005)

(0.042)

(0.043)

(0.043)

(0.052)

(0.05)

(0.049)

Unemployed

0.009***

0.007***

0.008***

0.008

0.005

0.006

–0.05*

–0.045

–0.051*

–0.074*

–0.066

–0.063

(0.002)

(0.002)

(0.003)

(0.005)

(0.005)

(0.005)

(0.029)

(0.03)

(0.030)

(0.042)

(0.042)

(0.043)

Inactive

–0.008***

–0.008***

–0.01***

–0.007*

–0.007*

–0.01**

–0.131***

–0.129***

–0.146***

–0.051

–0.048

–0.077

(0.002)

(0.002)

(0.003)

(0.004)

(0.004)

(0.005)

(0.037)

(0.037)

(0.037)

(0.051)

(0.05)

(0.05)

GDP last quarter

0.018***

0.031***

0.011

0.053

(0.003)

(0.007)

(0.04)

(0.061)

Unemployment rate last quarter

0.001***

0.001***

–0.004

–0.008*

(0.000)

(0.000)

(0.003)

(0.004)

Year dummies

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes

No

No. of observations

48,664

48,664

41,373

13,305

13,305

11,223

2,389

2,389

2,336

938

938

915

Pseudo R2

0.064

0.085

0.071

0.0526

0.0703

0.0638

0.1335

0.1368

0.1393

0.155

0.162

0.1694

Notes: The level of occupation corresponds to the standard categorization of the ISCO code: low (9), medium (4–8), and high (0–3). Overeducation was measured as a combination of education and occupational level. Overeducation among the medium educated was defined as the combination of medium education (ISCED 3 or 4) and low occupational level (ISCO 9); overeducation among the highly educated was defined by high education (ISCED 5 or 6) and a low or middle level of occupation (ISCO > 3). The figures reported in the table are the marginal effects with standard errors in parentheses. The reference categories in the regressions are male, single, age 55–65 years, Estonian nationality, primary education, overeducated among primary education, low-level education, and salaried employee.

(*) p < .10.

(**) p < .05.

(***) p < .01.

Second, we analyzed selection of returnees compared to current Estonian emigrants (see Table 16.6). Age affects selection for returning differently for emigrants than for stayers: The likelihood of returning increases with age. Therefore, younger aged people are more likely to experience temporary labor migration, but once abroad they are more likely to return as they grow older. Analysis of the young sample revealed that returnees are likely to be female (the (p.488) same holds in the total sample). This result, coupled with the evidence on selection by gender in the returnee–stayer framework, implies that men are generally more likely to choose temporary employment abroad, but once in the foreign country, women are more likely to return. Regarding job-related characteristics, young returnees are less likely to occupy medium- and high-level positions in the last quarter abroad. Overeducation in the last quarter abroad significantly affects the decision to return in the young subsample of both medium- and highly educated returnees. At the same time, overeducation only appeared to significantly affect the decision to return among the medium educated in the total sample. Among other employment-related variables, unemployed status a year previously decreases the likelihood of being a returnee in the young sample solely in model M10. Self-employed, student, and inactive labor market status a year previously plays no significant role in the selection of returning youth. Naturally, a higher unemployment rate in the home country is negatively associated with the likelihood of returning; however, a statistically significant effect was found only in the young subsample.

16.5.2.2. The Effect of Migration Status on Labor Market Status (Multinomial Logistic Regression)

Table 16.7 reports the results of the multinomial logistic regression of labor market status (employed, unemployed, or inactive) in the last quarter of the interview across the total sample and the youth subsample. In the baseline model of the young age group (M4), returnees were found to be 9.3 percentage points (pp) more likely to be unemployed and 21 pp less likely to be inactive. A similar pattern holds in the total sample, albeit of a smaller magnitude (6.2 pp and 6 pp, respectively). Regarding the effect of other controls within the youth sample, women are less likely than men to be unemployed, whereas they are more likely to be inactive. Married respondents are less likely to be either unemployed or inactive. Non-Estonians have a 5.1 pp greater likelihood of facing unemployment and are 8.1 pp less likely to be inactive. A higher education degree decreases the likelihood of unemployment by 1.9 pp, whereas the probability of being inactive is negatively and substantially affected by both secondary and higher education. Macroeconomic indicators appeared to have no statistically significant association with the odds of being unemployed or inactive in the young group (M6). However, model M3, based on the total sample, revealed a significant positive effect of the unemployment rate on the probability of unemployment and inactivity, whereas the GDP level negatively affects the likelihood of unemployment in the total sample.

Table 16.7 Estonia: Labor market status analysis

All sample

Young sample (15–34 years)

M1

M2

M3

M4

M5

M6

Unemployed

Inactive

Unemployed

Inactive

Unemployed

Inactive

Unemployed

Inactive

Unemployed

Inactive

Unemployed

Inactive

Returnee

0.062***

–0.06***

0.051***

–0.066***

0.057***

–0.064***

0.093***

–0.21***

0.082***

–0.219***

0.091***

–0.215***

(–0.004)

(–0.011)

(–0.004)

(0.011)

(0.004)

(0.011)

(0.007)

(0.021)

(0.007)

(0.021)

(0.007)

(0.021)

Male

0.019***

–0.086***

0.018***

–0.086***

0.021***

–0.084***

0.021***

–0.182***

0.02***

–0.183***

0.024***

–0.174***

(0.001)

(0.002)

(0.001)

(0.002)

(0.002)

(0.002)

(0.002)

(0.004)

(0.002)

(0.004)

(0.003)

(0.004)

Married

–0.019***

–0.065***

–0.019***

–0.065***

–0.018***

–0.061***

–0.011***

–0.286***

–0.011***

–0.286***

–0.011***

–0.274***

(0.002)

(0.003)

(0.002)

(0.003)

(0.002)

(0.003)

(0.003)

(0.004)

(0.003)

(0.004)

(0.003)

(0.004)

Age 15–24 years

0.041***

0.005***

0.043***

0.006*

0.048***

0.007*

(0.002)

(0.003)

(0.002)

(0.003)

(0.003)

(0.004)

Age 25–34 years

0.063***

–0.283***

0.065***

–0.282***

0.072***

–0.274***

(0.002)

(0.003)

(0.002)

(0.003)

(0.003)

(0.004)

Age 35–44 years

0.058***

–0.371***

0.059***

–0.369***

0.061***

–0.366***

(0.002)

(0.003)

(0.002)

(0.003)

(0.003)

(0.004)

Age 45–54 years

0.057***

–0.357***

0.059***

–0.355***

0.064***

–0.354***

(0.002)

(0.003)

(0.002)

(0.003)

(0.003)

(0.004)

Other non-Estonian nationality

0.041***

–0.013***

0.042***

–0.012***

0.045***

–0.019***

0.051***

–0.081***

0.052***

–0.079***

0.056***

–0.09***

(0.001)

(0.002)

(0.001)

(0.002)

(0.002)

(0.003)

(0.002)

(0.005)

(0.002)

(0.005)

(0.003)

(0.005)

Secondary education

0.005***

–0.141***

–0.002

–0.145***

–0.001

–0.156***

0.013***

–0.207***

0.008***

–0.21***

0.011***

–0.224***

(0.002)

(0.002)

(0.002)

(0.002)

(0.002)

(0.003)

(0.003)

(0.004)

(0.003)

(0.004)

(0.003)

(0.004)

Higher education

–0.027***

–0.226***

–0.033***

–0.23***

–0.035***

–0.237***

–0.019***

–0.361***

–0.025***

–0.367***

–0.029***

–0.359***

(0.002)

(0.003)

(0.002)

(0.003)

(0.003)

(0.004)

(0.004)

(0.006)

(0.004)

(0.006)

(0.005)

(0.007)

GDP per capita

–0.019*

0.016

–0.029

0.003

(0.011)

(0.016)

(0.021)

(0.029)

Unemployment rate

0.002***

0.002*

0.002

0.001

(0.001)

(0.001)

(0.002)

(0.002)

Year dummies

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No

Yes

Yes

No. of observations

143,017

143,017

143,017

143,017

111,069

111,069

51,559

51,559

51,559

51,559

39,609

39,609

Pseudo R2

0.0494

0.2562

0.0726

0.2568

0.0732

0.2543

0.0291

0.1766

0.0541

0.178

0.0529

0.1767

Note: See notes to Table 16.6.

16.5.3. Results: Slovakia

16.5.3.1. Selectivity Analysis

The results for the Slovak sample are presented in Table 16.8 for the general sample and for the youth subsample. Comparing returnee–stayer selection in (p.489) the general sample, we find that being male, young (aged 15–34 years), single (as opposed to married), and of Slovak nationality all increase the likelihood of being a returnee. Among young returnees, only being single increases the likelihood of return. Young returnees are also more likely to have been either a student or unemployed a year before the interview in the host country, relative to being employed, but are less likely to be economically inactive. Young returnees are also more likely to work in medium-skilled positions and are less likely to be self-employed compared to stayers. We observe similar results for the general sample. Concerning the macroeconomic variables, essentially the same results were observed for both the general sample and the youth sample (M5 and M6). A higher unemployment rate in the home country is associated with a lower probability of returning. Overall, although we find significant differences between returnees and stayers in both samples, they are substantively rather small. Importantly, we do not find any effect from overeducation, skill level of occupation, or the level of education on the selection of returnees relative to stayers.

Table 16.8 Slovakia: Selectivity analysis

Returnee–stayer

Returnee–emigrant

All sample

Youth sample (15–34 years)

All sample

Youth sample (15–34 years)

M1

M2

M3

M4

M5

M6

M7

M8

M9

M10

M11

M12

Male

0.002**

0.002**

0.002**

–0.001

–0.001

0

–0.001

–0.002

–0.002

–0.012

–0.014

–0.012

(0.001)

(0.001)

(0.001)

(0.002)

(0.002)

(0.002)

(0.011)

(0.011)

(0.011)

(0.018)

(0.017)

(0.017)

Married

–0.003***

–0.004***

–0.004***

–0.009***

–0.010***

–0.010***

–0.018

–0.018

–0.02

–0.03

–0.027

–0.029

(0.001)

(0.001)

(0.001)

(0.002)

(0.002)

(0.002)

(0.012)

(0.012)

(0.012)

(0.021)

(0.021)

(0.021)

Age 15–24 years

0.009***

0.008***

0.008***

0.008

0.008

-0.008

(0.002)

(0.002)

(0.002)

(0.025)

(0.025)

(0.026)

Age 25–34 years

0.006***

0.005***

0.005***

–0.001

–0.004

–0.011

(0.001)

(0.001)

(0.001)

(0.022)

(0.022)

(0.023)

Age 35–44 years

0.001

0.001

0.001

–0.024

–0.025

–0.029

(0.001)

(0.001)

(0.001)

(0.022)

(0.021)

(0.023)

Age 45–54 years

0

0

0

–0.03

–0.032

–0.035

(0.001)

(0.001)

(0.001)

(0.022)

(0.022)

(0.023)

Other non-Slovak nationality

–0.003**

–0.003**

–0.003**

–0.005

–0.005

–0.005

–0.022

–0.021

–0.022

–0.03

–0.034

–0.032

(0.001)

(0.001)

(0.001)

(0.003)

(0.003)

(0.003)

(0.014)

(0.014)

(0.014)

(0.023)

(0.023)

(0.023)

Secondary education

0.002

0.002

0.002

–0.001

0

0

0.011

0.005

0.014

–0.008

–0.015

–0.006

(0.002)

(0.002)

(0.002)

(0.008)

(0.008)

(0.008)

(0.03)

(0.031)

(0.028)

(0.061)

(0.062)

(0.059)

Higher education

–0.002

–0.001

–0.001

–0.007

–0.006

–0.005

–0.033

–0.039

–0.022

–0.049

–0.06

–0.033

(0.002)

(0.002)

(0.002)

(0.009)

(0.008)

(0.009)

(0.036)

(0.036)

(0.036)

(0.071)

(0.071)

(0.071)

Overeducated among medium educated

0.009

0.007

0.007

0.08

0.065

0.069

0.007

0.017

0.004

0.176

0.18

0.174

(0.007)

(0.007)

(0.007)

(0.069)

(0.057)

(0.06)

(0.044)

(0.046)

(0.043)

(0.147)

(0.141)

(0.145)

Overeducated among highly educated

0.002

0.002

0.002

0.003

0.004

0.003

0.007

0.013

0

0.003

0.016

–0.007

(0.004)

(0.004)

(0.004)

(0.008)

(0.008)

(0.008)

(0.048)

(0.05)

(0.046)

(0.057)

(0.062)

(0.053)

Medium-level occupation

0.001

0.000

0.001

0.014

0.012

0.013

–0.022

–0.011

–0.023

0.075

0.079

0.075

(0.004)

(0.004)

(0.004)

(0.011)

(0.010)

(0.010)

(0.046)

(0.043)

(0.046)

(0.066)

(0.063)

(0.066)

High-level occupation

–0.004

–0.005

–0.005

0.001

–0.001

–0.001

–0.025

–0.012

–0.03

0.048

0.054

0.04

(0.003)

(0.003)

(0.003)

(0.007)

(0.007)

(0.007)

(0.05)

(0.046)

(0.049)

(0.07)

(0.067)

(0.068)

Self-employed

–0.002

–0.002

–0.002

–0.008*

–0.008*

–0.008*

–0.100***

–0.099***

–0.091***

–0.125***

–0.128***

–0.118***

(0.001)

(0.001)

(0.001)

(0.004)

(0.004)

(0.004)

(0.017)

(0.017)

(0.017)

(0.032)

(0.032)

(0.032)

LM status one year ago—student

0.006*

0.006*

0.006*

0.013*

0.013*

0.012*

0.097*

0.101**

0.103**

0.124**

0.130**

0.125**

(0.003)

(0.003)

(0.003)

(0.005)

(0.005)

(–0.005)

(0.039)

(0.039)

(0.04)

(0.044)

(0.044)

(0.044)

Unemployed

0.004**

0.005**

0.005**

0.008*

0.010*

0.010**

0.063**

0.068**

0.072***

0.073*

0.078**

0.084**

(0.002)

(0.002)

(0.002)

(0.004)

(0.004)

(0.004)

(0.021)

(0.021)

(0.022)

(0.03)

(0.03)

(0.031)

Inactive

0

0

0

–0.008*

–0.008**

–0.008**

0.165

0.180*

0.183*

0.101

0.139

0.144

(0.002)

(0.002)

(0.002)

(0.003)

(0.003)

(0.003)

(0.085)

(0.086)

(0.089)

(0.18)

(0.191)

(0.2)

GDP last quarter

–0.002

–0.007

–0.056*

–0.091*

(0.002)

(0.005)

(0.025)

(0.041)

Unemployment rate last quarter

–0.001***

–0.002***

–0.008***

–0.006

(0.000)

(0.000)

(0.002)

(0.004)

Year dummies

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes

No

No. of observations

53,604

53,604

53,602

15,449

15,449

15,447

3,510

3,510

3,508

1,652

1,652

1,650

Pseudo R2

0.102

0.116

0.116

0.089

0.111

0.106

0.064

0.092

0.073

0.053

0.089

0.06

Note: See notes to Table 16.6.

Comparing returnees to current emigrants, we find no significant differences between these groups in terms of demographic characteristics. We focus on interpreting the results for the youth subsample. The only significant results relate to the nature of employment and previous labor market status. Being self-employed is associated with approximately 13 pp lower probability of being a returnee. Being a student or unemployed a year previously are all associated with a higher probability of returning (approximately 13 pp and 8 pp, respectively). Furthermore, higher GDP is negatively associated with the probability of being a returnee rather than a current emigrant, but we do not find a significant effect of unemployment rate in the youth subsample. Higher unemployment in the home country does, however, deter returns for the general sample.

16.5.3.2. The Effect of Migration Status on Labor Market Status (Multinomial Logistic Regression)

Table 16.9 shows the results of the multinomial logistic regression of labor market status (employed, unemployed, or inactive) in the last quarter of the interview across the total sample (M1–M3) and the youth subsample (M4–M6). We again focus on the interpretation of the youth subsample. Results from the baseline model of the multinomial logistic regression of labor market status in the last quarter of the interview (M4 in Table 16.7) show the probability of being employed, unemployed, or inactive for the whole sample. Compared to stayers and migrants, young returnees are 46 pp more likely to be unemployed—a strikingly stronger relationship compared to Estonia—and 30 pp less likely to be inactive, controlling for gender, age, marital status, education, and nationality. Women have a lower probability of being unemployed but a greater probability of being inactive compared to men. Being married decreases the chances of being (p.490) unemployed or inactive. Having a higher education decreases the likelihood of unemployment or inactivity, whereas having a secondary education increases the probability of unemployment. Non-Slovaks have a 12 pp lower probability of unemployment, but a 13 pp stronger likelihood of being inactive. These results also hold in the extended specifications of the model. The results for the total subsample are substantively the same on most accounts. Adding macroeconomic variables to the model (M3), we do not observe any effect from the level of GDP on unemployment or inactivity, but we still find a positive effect of rising unemployment rates on unemployment. For the total sample, a higher GDP level and unemployment rate are associated with a higher probability of being unemployed, but there is no such linkage with the probability of being inactive.

Table 16.9 Slovakia: Labor market status analysis

All sample

Young sample (15–34 years)

M1

M2

M3

M4

M5

M6

Unemployed

Inactive

Unemployed

Inactive

Unemployed

Inactive

Unemployed

Inactive

Unemployed

Inactive

Unemployed

Inactive

Returnee

0.415***

–0.188***

0.427***

–0.191***

0.432***

–0.191***

0.462***

–0.293***

0.476***

–0.296***

0.479***

–0.296***

(0.027)

(0.02)

(0.027)

(0.02)

(0.027)

(0.02)

(0.036)

(0.029)

(0.035)

(0.028)

(0.035)

(0.028)

Male

0.008***

–0.138***

0.008***

–0.138***

0.008***

–0.138***

0.022***

–0.198***

0.021***

–0.198***

0.021***

–0.198***

(0.002)

(0.002)

(0.002)

(0.002)

(0.002)

(0.002)

(0.003)

(0.004)

(0.003)

(0.004)

(0.003)

(0.004)

Married

–0.040***

0.005

–0.038***

0.006

–0.038***

0.006

–0.015***

–0.095***

–0.012**

–0.094***

–0.012**

–0.093***

(0.002)

(0.003)

(0.002)

(0.003)

(0.002)

(0.003)

(0.004)

(0.005)

(0.004)

(0.005)

(0.004)

(0.005)

Age 15–24

0.020***

0.054***

0.022***

0.055***

0.022***

0.055***

(0.003)

(0.006)

(0.003)

(0.006)

(0.003)

(0.006)

Age 25–34

0.081***

–0.363***

0.083***

–0.362***

0.083***

–0.362***

(0.003)

(0.005)

(0.003)

(0.005)

(0.003)

(0.005)

Age 35–44

0.085***

–0.462***

0.085***

–0.461***

0.085***

–0.461***

(0.003)

(0.004)

(0.003)

(0.004)

(0.003)

(0.004)

Age 45–54

0.083***

–0.449***

0.083***

–0.449***

0.083***

–0.449***

(0.003)

(0.004)

(0.003)

(0.004)

(0.003)

(0.004)

Other non-Slovak nationality

–0.080***

0.039***

–0.079***

0.039***

–0.079***

0.039***

–0.121***

0.126***

–0.120***

0.126***

–0.120***

0.126***

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.007)

(0.007)

(0.007)

(0.007)

(0.007)

(0.007)

Secondary education

–0.108***

–0.278***

–0.110***

–0.277***

–0.110***

–0.277***

0.034***

–0.565***

0.034***

–0.565***

0.034***

–0.565***

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.004)

(0.005)

(0.004)

(0.005)

(0.004)

(0.005)

Higher education

–0.162***

–0.312***

–0.165***

–0.312***

–0.165***

–0.312***

–0.017***

–0.619***

–0.021***

–0.622***

–0.022***

–0.622***

(0.004)

(0.005)

(0.004)

(0.005)

(0.004)

(0.005)

(0.005)

(0.007)

(0.005)

(0.007)

(0.005)

(0.007)

GDP per capita

0.028***

–0.012

0.003

–0.002

(0.007)

(0.009)

(0.003)

(0.004)

Unemployment rate

0.004**

–0.003

0.040**

–0.040*

(0.002)

(0.002)

(0.012)

(0.016)

Year dummies

No

Yes

No

No

Yes

No

No

Yes

No

No

Yes

No

No. of observations

96,820

96,820

96,818

36,259

36,259

36,257

Pseudo R2

0.254

0.2558

0.2559

0.2212

0.2252

0.2254

Note: See notes to Table 16.6.

16.6. Comparative synthesis

The conclusions from the Estonian and Slovakian case studies contribute to previous empirical findings regarding the post-return labor market performance of return migrants, and they also reveal the main characteristics of the labor market integration of young returnees in two small economies in Central and Eastern Europe.

We find a multitude of differences in the return migration patterns, determinants of selection, and labor market integration of returnees. First, return migration is a more widespread phenomenon in Estonia than in Slovakia. In Estonia, net intra-EU migration is positive because more people have started to return than to leave. The Slovak balance continues to be negative. Poor labor market conditions could be the reason for continued outflows of migrants from Slovakia. Second, young returnees do not differ from young stayers or young emigrants in terms of their level of education in either of the two countries. However, Estonian returnees in the total sample are positively selected on the basis of education relative to stayers and migrants. The no-effect findings for youth seem to contradict other studies finding selectivity on the basis of education (Hazans and Philips 2010; Martin and Radu 2012; see also the literature review in Section 16.2), but these studies did not specifically focus on youth.

Third, overeducation plays no role in the selectivity of returnees relative to migrants or stayers in Slovakia. This is in line with other research using web-survey data about returnees (Kureková and Žilinčíková 2018). Kureková and Žilinčíková show that returnees find positions equivalent to their qualifications after returning and that mismatch does not cause a failed return; in other words, there is no negative effect of a mismatch on Slovak returnees. The results are significant in Estonia, where overeducation among highly educated young return migrants has contributed to their return. This finding is in line with several other studies, which argue that a mismatch abroad is a significant factor of return (Currie 2007; Pungas et al. 2012; Coniglio and Brzozowski 2016). This (p.491) suggests that young highly educated Estonians face difficulties when trying to find a job that corresponds to their qualifications abroad and that the decision to return is partly driven by a mismatch in their occupation and qualifications in the foreign labor market. It may also indicate that highly educated Estonian youth are relatively optimistic about their opportunities in their home country. In the total sample of highly educated Estonians, no statistically significant effect of overqualification on return probability was found.

The patterns observed regarding overeducation in Estonia could be explained in terms of young people gaining more from their good education in the home country compared to older people. Although generally the returns on higher education are high in the Estonian labor market, some labor market groups, such as ethnic minorities, benefit much less from higher education (Hazans 2003). The main destination countries have to be acknowledged in this context, too. Masso et al. (2016) showed that Finland was and remains the key destination country among Estonian emigrants.8 A highly suppressed income distribution in Finland coupled with the previous evidence on occupational downgrading of Estonian migrants (Masso et al. 2014) may result in lower earnings for highly educated Estonian migrants who fail to find a job that corresponds to their qualifications. At the same time, a lower occupation–qualification match for medium-educated young Estonians in Finland results in higher earnings compared to a better match if they were to remain in Estonia. In other words, they obtain higher earnings in Finland compared to Estonia despite their lower occupation–qualification match. The latter finding is supported by the negative effect of overeducation among the medium educated on selection of returnees.

Fourth, for the young Estonian returnees, labor market status a year previously does not affect their selectivity relative to migrants or stayers, whereas it is an important factor for the Slovak returnees. The crucial role of labor market conditions in Slovakia is also confirmed in the analysis of post-return short-term labor market outcomes. Although we find a higher risk of short-term unemployment for young returnees in both countries, there are some important cross-country differences. The magnitude of the negative effect of returnee status on labor market performance is much stronger in Slovakia than in Estonia. Furthermore, the impact of macroeconomic variables in Estonia is less important in predicting labor market outcomes for young and older returnees. The latter finding might be related to the rather different destination countries of the Estonian and Slovak migrants and possibly to the fact that the business cycles in the home and host countries for migrants are more closely correlated in the case of Estonia. The finding that being a returnee has a negative impact on short-term labor market outcomes is generally in line with the findings of other studies (Smoliner et al. 2012; Coniglio and Brzozowski 2016). We can, however, also anticipate that most returnees integrate relatively smoothly within 6 months of return, as has been shown in other research, not least due to their high levels of education and foreign experience. For example, Tverdostup and Masso (2016) (p.492) identified a positive, statistically significant effect of temporary mobility on earnings in the young cohort 3 years after returning (based on Estonian Population and Housing Census data linked to Tax Registry data on individual payroll taxes). This result is in line with our finding of a negative short-term impact on labor market performance and suggests that positive returns on foreign labor market experience for youth develop over time after returning home. Masso et al. (2016) found that employers and young returnees generally value foreign work experience positively, although, on the negative side, employers mention higher wage expectations among returnees and the risk of them going abroad again in the future. These authors also document that unemployment benefits appear to facilitate job matching after return, but likewise temporarily increase short-term unemployment as returnees use the time to find adequate jobs. Finally, Masso et al. found that foreign work experience significantly increases the attractiveness of job candidates.

The initial differences in the likelihood of unemployment between the Slovak and Estonian returnees are probably a function of the general performance of the labor market, which has been relatively poor in Slovakia. The labor market situation in the host countries has important implications for the ease of reintegration of returnees. It might also explain the differences in the magnitude of returns, which have been more prominent in Estonia and comparatively weaker in Slovakia. Overall, the labor market situation in the home country affects return decisions and labor market performance. It appears that better labor market conditions and increased welfare support in response to the crisis have contributed to better immediate labor market outcomes for Estonian returnees. Other studies suggest that medium-term integration prospects for returnees are likely to be better relative to the situation immediately after return; that is, over time the prospects of reintegration into the home country labor market are likely to improve (Piracha and Vadean 2010; Masso et al. 2016).

16.7. Conclusions

This chapter furthers our understanding of the selectivity and labor market integration of return migrants in Estonia and Slovakia. The comparative approach is useful because it helps highlight that selectivity and integration prospects might vary significantly across EU countries. Our findings highlight the complex ways in which various factors intervene and interrelate in affecting different subgroups of returnees (e.g., young returnees) in different ways, including a mediating role of personal, gender, and family-related factors that we are unable to uncover in our analysis. The complexity is further revealed in the two-country comparison showing that across countries, different factors might play a role, depending on, for example, home country labor market conditions. In summary, our research seems to point to different underlying reasons for mobility and return in Estonia (p.493) and Slovakia, mediated by the role of labor market performance and welfare spending changes. This implies that no uniform conclusions or policy advice that is applicable across the EU are possible in the area of return migration and that specific country contexts should be carefully investigated and evaluated.

We have focused, in particular, on isolating the role of macroeconomic factors in affecting who returns and how they integrate. Although we have been unable to investigate the full range of possible factors, our findings suggest that the quality of the macroeconomic environment affects both the selectivity and the performance of returnees. Better labor market conditions in Estonia and significantly enhanced social support in response to the crisis appear to have encouraged the return of older migrants and facilitated the reintegration of young migrants.

Although our study shows that in both countries, returnees initially enter unemployment registers, evidence suggests that this is a temporary phenomenon facilitated by the possibility of transferring unemployment benefits from the country where they were earning to another EU country (typically the home country) for a period of 3 months. Other research rather consistently shows that the integration prospects of returnees improve soon thereafter and that they find work within 6 months. Employers value foreign work experience because it demonstrates a set of skills valued in the CEE labor markets. A further important finding relates to the role of overeducation and mismatch in shaping return patterns. Especially in the case of Estonia, a mismatch abroad led to a greater propensity to return among highly educated young returnees, but it disincentivized the return of medium-educated migrants. This suggests that receiving countries are losing the most able CEE migrants because of a failure to offer quality employment and career prospects. Although this appears to be an advantage for the sending countries, it is unlikely that these highly educated returnees had enough opportunities to develop their human capital and that, therefore, their contribution to the home country is more limited.

The limitations of our chapter are threefold. First, we only examine how different labor market groups—returnees, stayers, and current emigrants—perform in terms of labor market status. Such an approach naturally has its limitations because return migration might also have an effect on wages (Hazans 2008), the tendency to be self-employed, or occupational mobility (Masso et al. 2014). Second, given the data structure, we are only able to analyze short-term labor market outcomes in the 3 months following the return. Although the results indicate a worse labor market situation for returnees than for emigrants and stayers, other research consistently finds that in the longer term, returnees integrate well and their foreign work experience is valued in the domestic labor market after returning (Masso et al. 2016). Third, because of data limitations, we concentrate on economic factors only and are unable to consider several other factors, such as social networks and some of the specific characteristics of migration that arguably play a role in successful reintegration (Barrett and Mosca 2013; Coniglio and Brzozowski 2016). Although most of the returnees had experienced short-term (p.494) migration, we were unable to reconstruct the exact length of the migration spell that was previously found to increase difficulties with integration upon return (Coniglio and Brzozowski 2016). We could also not employ a measure of the number of children in our analysis, which had been found to have a positive impact on integration into the home labor market (Coniglio and Brzozowski 2016). Last, we were unable to control for the destination country of emigrants, which might have impacted on the selectivity of return and on integration into the home labor market, given the different employment opportunities in each host country.

One possible solution to some of these issues would be to have panel data following the whole migration process and return—capturing information for before migration (in the home country), while abroad (in the host country), and after return (once back in the home country). Such data, whether collected on a continuous basis or through a series of retrospective interview surveys, would capture the complete migration path and examine the selections more profoundly. It would allow us to analyze “true” returns on migration and returning home in a consistent manner, controlling for migrants’ labor market performance in the home country before leaving. This kind of data could also be obtained by linking the national registers of home and host countries (e.g., Estonia and Finland). However, the downside of such an approach is that we are likely to learn only about a limited number of countries, which may induce some selectivity issues. Online data, such as reconstructing life histories from online curriculum vitae (CVs), provide another possible source for studying migration and returning home from the perspective of labor market integration (Kureková and Žilinčíková 2018).

Several policy lessons can be drawn from our analysis. First, given that young return migrants constitute a specific subgroup of the returnee population, they should be attracted to the host-country economy because they have significant potential based on high educational attainment accompanied by foreign market experience. Facilitating the acceleration of the labor market integration of young returnees will enable them to fully realize their potential and thus provide benefits for the home-country economy. There is scope for public institutions to provide better assistance upon return and to facilitate integration, especially in underperforming labor markets such as that of Slovakia. Precisely such practices of labor market intermediaries were also identified for EU8 migrants in Austria (Ortlieb and Weiss, this volume). For example, return migrants can become a target category for post-return assistance in labor offices, especially if they return to worse performing regions, as seems to be the case (Barcevičius et al. 2012).

Second, inequalities exist among returnees, and not all returnees are on an equal footing in terms of their abilities. In particular, returnees disadvantaged in terms of gender, age, ethnicity, or geographic location might be in more need of assistance from public authorities in their reintegration process. On the other hand, programs targeted at highly educated youth underperforming in the host (p.495) countries may help overcome the effects of a brain drain or brain waste. Yet, as demonstrated in the Slovak case, given that overeducation need not be associated with the return decision among the highly skilled, the challenge could also be how many opportunities the home-country labor market offers these individuals. The need for policy intervention seems to be somewhat less pressing in the Estonian case, in which overeducation was shown to be associated positively with returning home among the highly educated.

(p.496)

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Notes:

(1) Jaan Masso acknowledges financial support from the Estonian Research Agency, project No. IUT20-49, “Structural Change as the Factor of Productivity Growth in the Case of Catching Up Economies.” The authors are grateful for comments made on earlier versions by Maura Sheehan, Jan Brzozowski, and the editors of this volume, while assuming full responsibility for the final content.

(2) The mechanism of transfer of unemployment benefits allows an individual to carry over unemployment benefits from the EU country in which he or she was last working to another EU country, usually for a period of 3 months. There are two basic conditions under which a worker is entitled to transfer the benefits. First, the worker must be entitled to unemployment benefits in the country of last employment and, second, he or she must register as unemployed with the labor office in another EU member state. The eligibility, duration, and maximum amount of benefits vary widely across EU countries. For example, the level of jobseeker’s allowance in the United Kingdom is relatively low—approximately £313 per month for a person aged older than 25 years, which is extremely difficult to live on. The relative value of such benefits may be higher in the home country, where living costs may be lower; hence, an unemployed person might choose to return home to receive this value of benefits in his or her country of origin.

(3) Kureková and Žilinčíková (2018), analyzing online CV data, find that return migration to Slovakia is much more sizable. In their sample of young jobseekers, every fifth person had experience of migration. Their sample also significantly differs from the EU-LFS sample of returnees regarding key demographic characteristics, especially the education variable.

(4) One may think of the higher unemployment rate among returnees as being related to the scarring effect if the best people do not emigrate. However, the qualitative evidence shows that returnees are rather attractive for employers but that they may have higher wage expectations, resulting in a longer job search period (Masso et al. 2014, 2016). The higher unemployment rate may also be due to savings accumulated abroad that enable returnees to afford a longer period for job search.

(5) We are grateful to Jan Brzozowski for drawing our attention to this possibility.

(6) The higher share of return migrants among youth may be thought to be associated with student mobility; however, in the current analysis, the definition of returnee is based exclusively on being abroad for work.

(7) The results are probably due to the selection rather than, for example, to individuals previously employed in medium-level positions moving to high-level positions because of return migration, given that previous studies did not find any effect of return migration on occupational upgrading (Masso et al. 2014).

(8) The evidence from the Estonian job search portal data set (CV Keskus) revealed that among Estonian migrants aged 15–35 years, the share of those moving to Finland increased from 17% in 2004 to 38% in 2012 (Masso et al. 2016).