Crossing the Border for Higher Status? Occupational Mobility of East–West Commuters in the Central European Region

Abstract

This article discusses cross-border occupational mobility of workers from the Czech Republic, Hungary, and Slovakia who found employment in Austria. We illustrate this topic using data from a longitudinal survey on the employment careers of 1,347 cross-border commuters working in Austria conducted in the winter/spring of 2012/13. Empirically, we demonstrate that despite efforts to diminish structural inequalities between the regions, differences in unemployment and poverty rates continue to play a role. We further find that the majority of cross-border commuters have a different socioeconomic structure than locally residing Austrians overall commuters mostly show mid-level qualifications and work in branches with a high demand for labor (e.g., construction, gastronomy, health industry). Against this background, we pursue the following research question: to what extent are the patterns of occupational mobility of cross-border commuters in Austria influenced by sociodemographic factors, human capital, social capital, and labor market characteristics?

Keywords:

• Central European Region
• cross-border commuting
• occupational mobility
• transnational labor market
• wage mobility

Investigating new facets of transnational labor markets has gained increasing attention in international migration research and an increasing body of empirical studies deals with the various facets of transnational labor markets (Fassmann and Kollár 1996; Gottholmseder and Theurl 2007; Lutz and Palenga-Möllenbeck 2011; Strüver 2005; Verwiebe et al. 2015). According to Portes, Guarnizo, and Landolt (1999: 219), labor markets are defined as transnational when they constitute “occupations and activities that require regular and sustained social contacts over time across national borders for their implementation.” Transnational labor markets bridge immigrants’ home and host societies and serve to realize the potential of job opportunities, higher income returns, and social mobility for transnational migrants (Yang 2006: 176).

Against that background this article deals with an emerging transnational labor market in the Central European Region (Centrope). Since May 1, 2011, when, at the end of the seven-year transitional period, the final barriers were lifted, citizens of the Czech Republic, Slovakia, and Hungary received full access to the Austrian labor market. The transnational opportunity structures as well as the supply and demand of workforce in the Central European Region have been transformed ever since. We study Centrope’s transnational labor market while focusing on key aspects of cross-border commuting, which has received comparatively little attention in academic research. What we do not know yet is whether transnational labor activities lead to an actual improvement of cross-border commuters’ wages and occupation in comparison to their preceding job in the home country. So far no systematic studies in this field have been conducted. Until now the research literature on transnational labor activities and inequalities focus on sociodemographic differences such as gender, education, branches, or generational effects (e.g., Elrick and Lewandowska 2008; Lutz and Palenga-Möllenbeck 2011; Scott 2006). However, occupational mobility patterns in the course of cross-border commuting are of crucial societal relevance, particularly regarding path dependencies of early employment trajectories on later labor market outcomes but also in the context of regional divergence: the movement of workers from one European Union (EU) country to another has become an increasingly important adjustment mechanism for the European economy (European Commission 2015).

Cross-border commuters in the Central European Region are integrated in multiple social and economic systems. Their transnational involvement implies that their professional life is based in a different territory from their social environment. Given unequal wage levels and labor market conditions in the sending and receiving regions, the question arises whether and to what degree cross-border commuting is related to occupational mobility.¹ Our guiding research question in this study is: To what extent is the cross-border commuters’ probability of upward wage mobility, upward class mobility, and changes in the occupational field in Austria influenced by sociodemographic factors, social capital, and labor market characteristics? With this analysis of three related but independent dimensions of occupational mobility and its micro logic we want to contribute to a better understanding of the various facets of possible status changes that are linked with cross-border commuting in the Central European Region.

CONCEPTUAL FRAMEWORK

The European Union is a specific mobility space shaped by its own institutional and legal regulations (Favell and Recchi 2009). One characteristic of this European space is that the social composition of the mobile population has been shaped over time by the process of European integration. In the 1960s and 1970s, the more developed European economies imported labor from Mediterranean countries, especially for lower-skilled jobs (Castles 2006: 742). The dominating form of migration was a permanent shift in one’s place of residence. The beginning of the twenty-first century has seen a diversification of the patterns and forms of migration. We can observe an increase in the mobility of service-industry workers with diverse and often high-level qualifications, in temporal and circular migration, and in the variety of European regions from which mobile workers originate, including an increase in movements from Eastern Europe (Braun and Arsene 2009: 29; Verwiebe, Wiesböck, and Teitzer 2014b: 127).

Although much research has been devoted to transnational migration and the differentiation of types of EU migration since the eastward enlargement (e.g., Kahanec and Zimmermann 2010), cross-border commuting as a specific form of geographic labor mobility has received less attention. Our analysis provides input to this field by focusing on mobility from the border regions of the Czech Republic, Slovakia, and Hungary to Austria. In this region there are 6.5 million inhabitants whose life chances—in spite of some economic catching up in the new member states—remain characterized by an East–West divide. This reflects a new intricate economic geography as Austria’s neighboring countries underwent a complex process of societal transformation to a free society and a market-based economy prior to their EU accession (Kahanec and Zimmermann 2010).

One of the main arguments for cross-border labor commuting is associated with push-and-pull factors related to regional differences in wages and employment opportunities (Knotter 2014). It is argued that the probability of mobility is positively related to the size of any income differential (Frigyes and Ward-Warmedinger 2006). Jennissen and Wilhelmina (2005) showed that gross domestic product (GDP) per capita has a positive effect and the unemployment rate has a negative effect on net migration in the EU. The focus of our study is not the reasons for mobility but whether commuters are actually able to improve their social status by the act of transnational commuting. Therefore, it is crucial to differentiate the broad field of economic and sociological research concerning migration, which can be divided into three comprehensive areas: studies analyzing the decision processes leading to migration, studies dealing with the economic performance of mobile people in the destination country, and studies focusing on the economic impact of migration on the destination country (Fertig and Schmidt 2002: 7). Here we focus on the second field, but all fields are interrelated (e.g., understanding the composition of commuters is an important prerequisite for the analysis of their economic performances).

Our research is guided by several assumptions. We assume that processes of occupational mobility differ by gender. Recent studies on European cross-border labor mobility in Europe have shown that men are more willing to commute than women (Gottholmseder and Theurl 2007; Paci et al. 2010). Therefore, we expect men to have more extensive knowledge of the labor market in the region and thus more opportunities for the improvement of their status. Gender differences in terms of wages have frequently been shown in labor market and migration research (Giesecke and Verwiebe 2008; Livingston 2006; Zaiceva 2010). However, there is high demand for workers in certain female-dominated branches of the Austrian labor market—for example, care, health, ande hospitality—with a higher wage level than comparable branches in Hungary, Slovakia, and the Czech Republic. This could lead to the opposite assumption that women have higher chances than men to improve their occupational status through cross-border commuting. Moreover, we suppose that young commuters have higher chances for status improvement. Paci et al. (2010) pointed out that mobility in a cross-border labor market is a phenomenon marked by younger individuals. As young commuters possess less prior work experience, they have a bigger scope and potential for status improvement. However, one could also argue that above all specific work experience is needed on the highly segmented and specialized Austrian labor market. This could lead to better mobility chances for older workers with more occupational experience.

We further expect that the level of human capital is crucial for the wages of cross-border commuters. There is theoretical and empirical evidence that wages rise with the level of education (Becker 1993). It has also been shown that adequate language skills lead to higher wages (Ferrer, Green, and Riddell 2006; Verwiebe et al. 2015). Moreover, cross-border commuters’ occupational mobility patterns are influenced by the extent of usable social capital. Social capital can be defined as “investment and use of embedded resources in social relations for expected returns” (Lin 2000: 786). Such capital may compensate for the lack of core human capital (Waldinger, Lichter, and Ira 2003). As a focus of this study social networks are conceived as involving potentially valuable information that can be applied for processes of occupational mobility. In addition, branches play a role when it comes to occupational mobility patterns of cross-border commuters, as many growing areas in the service sector (e.g., hospitality, gastronomy, care) pay rather low wages in the presence of currently high labor demand and lower barriers to access than industrial production that is tightly organized by trade unions (Bittner, Hudler-Seitzberger, and Neunteufl 2011; Traxler et al. 2008; Verwiebe et al. 2014a). Finally, we assume that the frequency of cross-border commuting has an impact on occupational mobility chances. Seasonal commuting usually implies lower chances for status improvement, as the demand for workforce is limited to a certain season of the year. Weekly commuting is often associated with employment in private households, in particular in the field of care. Daily commuting, on the other hand, involves shorter distances and working on a regular base. We reason that in comparison with other forms of commuting, daily commuting is more likely to be linked to formal jobs and therefore more likely to enhance occupational mobility.

STATE OF THE RESEARCH: TRANSNATIONAL LABOR ACTIVITIES AND OCCUPATIONAL MOBILITY

Over the past two decades, the field of transnational labor research in the context of the EU has become more important (e.g., Ban 2012; Fassmann and Kollár 1996; Favell 2008; Kalter 2011; Recchi 2009; Strüver 2005; Verwiebe and Eder 2006; Verwiebe et al. 2014b). However, up-to-date studies on occupational mobility as an outcome of transnational labor activities remain scarce. In the literature the analytical emphasis in the field of wage mobility lies on wage effects in the receiving country, mainly due to the lack of longitudinal transnational data. For example research in this field focuses on effects of geographical labor mobility on wages in the destination region (Alsos and Eldring 2008; Baas, Brücker, and Hauptmann 2010; Friberg, Tronstad, and Dølvik 2012; Lemos and Portes 2014), wage assimilation of foreign-born workers to natives (Constant and Massey 2005; Kim 2013), wage mobility of immigrants within the destination country (Caparrós Ruiz 2014), intergenerational wage mobility of second-generation migrants (Flake 2013), or reservation wages of migrants, meaning the lowest wage rate at which a worker would be willing to accept a particular type of job (Constant et al. 2016). There is a substantial gap in the research literature on potential wage and status gains of geographical mobility itself in terms of the change of individual occupational activities from one region to another. This is particularly the case in the Central European Region.

In recent research there have been quite a few studies especially on upward mobility chances of immigrants in Spain. Simón, Ramos, and Sanromá (2014) demonstrated that the occupational status of immigrants in the Spanish labor market is substantially worse than in their countries of origin. Stanek and Ramos (2013) analyzed determinants of occupational mobility recorded for immigrants between their last job in the region of origin and their first job in Spain. They found that ethnic segmentation in the Spanish labor market negatively affects the occupational mobility of immigrants. Furthermore, non–EU-15 immigrants in their study are at higher risk of downward mobility. According to their findings higher levels of education offer protection against downward mobility and increase the chance for upgrading. Lastly, contrary to the authors’ presumptions, social support received from friends and relatives who reside in the destination country increases the risk of occupational downgrading. Vidal-Coso and Miret-Gamundi (2014) added the gender perspective and showed that female migrants to Spain are more likely to experience occupational downgrading at the time of migration than their male counterparts. Fernández-Macías et al. (2015) analyzed the crucial role of particular sectors (e.g., construction, cleaning) in determining occupational mobility patterns.

Most of the research in this field focuses on effects of long-term immigration, yet in our study we are interested in the patterns of short-term mobility on an emerging transnational labor market. A study conducted by Masso, Eamets, and Mõtsmees (2014) investigated the relation between temporary migration and upward mobility chances in Estonia. The authors studied whether temporary migration in a person’s career is associated with upward movement in the occupational ladder in the long run and did not find any positive effect. The authors related their findings to the short-term nature of migration and the occupational downshifting abroad (e.g., during a short-term employment in Finland) as well as the specific segmentation of the Estonian labor market, which allows little upward mobility across educational and class boundaries. Similarly, Kogan and Weißmann (2013) argue that in Germany a strong horizontal and vertical segmentation of the labor market favors the concentration of short-term migrants in certain occupational niches—regardless of their previous occupation—and thus restricts upward mobility chances.

Overall we can see that in the field of transnational labor research in the EU there is a substantial lack of research on cross-border commuting that addresses geographical proximity of residency and workplace as well as different wage levels and purchasing power parities. In this article, we try to close this gap by taking the Central European Region as an example.² Based on the longitudinal data conducted in the course of the TRANSLAB project, we are able to analyze whether specific status changes occur in the course of these transnational labor market activities.

STRUCTURAL AND INSTITUTIONAL CHARACTERISTICS OF THE CENTRAL EUROPEAN REGION

On the institutional level, the Central European Region is an EU-designed cross-border region (“Euroregion”) fostering enhanced mobility to strengthen competitiveness and the regional economy. However, the establishment of Euroregions is fundamentally connected with several political visions such as a Europe without borders, a new regionalism, and a turn back to (imagined) common regional historical and regional cultural roots (Klatt and Herrmann 2011: 66). In Centrope, mobility of labor is one of the core aims, emphasizing commitment to collaboration that ranges from research and innovation to human capital development, spatial integration, and culture and tourism. It reflects the differentiation of movement patterns within the EU: from once-in-a-lifetime migration to mobility as a life strategy. This development is also due to the relatively short distances within Centrope. For example, within one hour’s time someone can easily commute between Bratislava and Vienna, the two most populous metropolitan areas in Centrope (Figure 1).³

FIGURE 1 The Central European Region.

Historically the dissolution of the Soviet Union, the EU enlargement, and the removal of the last barriers to the free movement of labor have brought about significant changes that, in turn, have led to new forms of interplay between economic, social, political, and legal factors (Haller and Verwiebe 2016; Horvath 2012). Since May 1, 2011, when the final barriers were lifted at the end of the seven-year transitional period, citizens of the A8 countries have had full access to labor markets across the EU-27.⁴

Cross-border commuting within this part of Europe is not a recent phenomenon linked to the creation of the European common market: formal and informal circularity between the countries has existed for many decades, albeit under specific conditions in the period of state socialism and with a long history of cross-border mobility that began during the time of the Habsburg monarchy (Haller and Verwiebe 2016; Verwiebe et al. 2015: 4). With regard to recent trends, already in 2009, a slight but steady increase in the number of workers from neighboring countries began, from 11,591 commuters working in Austria in 2008 to 14,465 in 2009 (Austrian Labor Market Service; authors’ calculations). This reflects the gradual opening of the labor market before the last barriers to free mobility were lifted. From then on, a steady increase of East–West cross-border workers can be observed. In 2014, a total of 82,906 workers were registered on the Austrian labor market: from Hungary (39,886), from Slovakia (35,367), and from the Czech Republic (7,653) (Statistics Austria 2016).⁵ The rise in cross-border labor flows to Austria is expected to further continue in the future.

Concerning socioeconomic key data, labor market regulations, and the structures of welfare and educational systems, the region is still highly diverse; yet recent macro indicators show convergence in some areas. In Table 1 we display unemployment and poverty rates as well as household incomes for the subregions in the Central European Region since the year 2006.

TABLE 1 Regional Statistics Between 2006 and 2015

	Unemployment rate in percent			Poverty rate in percent			Household income per capita (PPS)
	2006	2012	2015	2006	2012	2015	2006	2012	2013
Austria	4.7	4.9	5.7	12.6	14.4	13.9	21,100	23,300	23,100
Lower Austria	4.0	4.6	5.2	12.1	9.9	9.0	21,600	24,100	24,000
Vienna	8.8	8.9	10.6	17.2	19.2	19.0	21,700	22,900	22,500
Burgenland	5.0	4.6	5.2	12.4	14.3	9.0	20,000	22,400	21,900
Czech Republic	7.1	7.0	5.0	9.9	9.6	9.7	11,400	12,300	12,500
Jihovýchod	7.1	7.6	4.9	8.3	7.0	7.9	10,500	11,900	12,400
Slovakia	13.4	14.0	11.5	11.6	13.2	12.3	8,700	11,600	12,200
Bratislavský kraj	4.6	5.7	5.7	7.3	6.3	7.3	15,900	21,500	22,300
Západné Slovensko	9.8	11.3	9.7	10.4	11.9	10.1	8,500	11,300	11,800
Hungary	7.5	11.0	6.8	15.9	14.3	14.9	9,000	9,500	9,800
Nyugat-Dunántúl	5.7	7.5	3.8	—	—	—	8,300	9,300	9,600

Source: Eurostat (2017).

Notes: Unemployment: percentage of working-age population (15 years and older); poverty: at risk of poverty, percentage of population; poverty rate for Hungary only available for NUTS-1 region Dunántúl; data for poverty rate for Austrian subregions 2012 and 2015 from Statistics Austria (2017) EU-SILC; household income: balance of primary income, net (uses), purchasing power standard (PPS) based on final consumption per inhabitant; data not available for 2015.

In terms of the unemployment rate one can observe convergence within the Central European Region. In most parts of Austria, especially in Vienna, unemployment is on the increase. Interestingly in the Slovakian, Czech, and Hungarian parts of Centrope it is the other way around: unemployment is decreasing. This is mainly due to the economic development in the region. For example, Slovakia and the Czech Republic became two of the fastest growing EU economies in the past decade with much higher GDP growth rates than in Austria (Eurofound 2017; OECD 2017). However, various (additional) factors influence the unemployment rate, rapid technological advances in particular sectors or demographic changes among others, and the level of unemployment benefits varies strongly between Austria on the one hand and Slovakia, Hungary, and the Czech Republic on the other.⁶

Using household income in purchasing power standard (PPS) as another example, there are still significant differences in household income between the Austrian, Slovakian, Czech, and Hungarian parts of Centrope. Yet in all neighboring regions of Austria, household incomes are increasing, thus leading to a gradual convergence of household income in the entire region. This development is most evident in Bratislava, where household income (€22,300 in PPS) reached the level of Vienna (€22,500 in PPS). In terms of risks of poverty,⁷ we observe decreasing or stable levels of poverty in all subregions of Centrope with the exception of Vienna, which has the highest concentration of people living in poverty in Centrope. There is no clear sign yet of a convergence of poverty risks in the region. And one also has to consider that the risk of poverty varies across subpopulations. For instance, women remain at a higher risk of poverty than men, even though the poverty rate of women is generally on the decline, falling faster than the EU-27 average (Fábián et al. 2014; Kahanec et al. 2014).

DATA, METHODS, SAMPLE DESCRIPTION

Regarding the methodological design, the TRANSLAB project builds on the established methods of “ethnosurvey” data collection (Massey 1987) and on more recent associated applications for the European setting (Kalter 2011; Massey, Kalter, and Pren 2008; Mullan and Frejka 1995; Wallace and Vincent 2007). From October 2012 to June 2013, the social research institute GfK Austria and its Central European partner institutes carried out face-to-face interviews with a total of 1,347 commuters to Austria and a reference group consisting of 1,340 noncommuters. By commuting, we understand a given individual’s repeated and regular (daily, weekly, monthly, or seasonal) movement across national borders. The interviewees were currently employed (working at least 20 hours a week), were 21 to 65 years old, and had their primary residence within regions bordering on Austria, for example, South Moravia (Czech Republic), Bratislava and Trnava (Slovakia), and Győr-Moson-Sopron and Vas (Hungary). The questionnaire collected information on reasons for cross-border commuting and noncommuting, current occupation, geographical mobility, human capital, network integration, and social demography. We further gathered detailed longitudinal information on the respondents’ employment careers using retrospective questions.

To adequately represent the regional structures of the countries of origin, commuters were defined as sampling points on the basis of district-specific information and classes of commune sizes per clustering procedures. The resulting sample thus covered a large scope of urban centers (e.g., Bratislava, Brno, and Győr), smaller towns, and border region villages. Little comprehensive information was available on the labor market integration of cross-border commuters from the Czech Republic, Slovakia, and Hungary in Austria. As the best available approximation, we obtained the subsample of commuters via quota sampling, drawing on labor market statistics of the Austrian Public Employment Service (AMS). Based on these data, we used information on the gender and age patterns of Hungarian, Czech, and Slovakian employees in Austria who had their residence outside of this country. To be able to ensure systematic comparisons between the mobile and nonmobile residents of the respective border regions in our sample, the subsample of noncommuters was also based on quota sampling. However, for this group, it was possible to elaborate detailed quotas for gender, age, and education on the basis of official statistics.⁸

Dependent Variables

Our survey contains detailed information on five job episodes: the current job of cross-border commuters in Austria and the last four jobs in their home region. We use this survey for several logistic regressions, which determine the probabilities for wage, class, and occupational mobility connected with cross-border commuting. The following dependent variables were used for our regression analyses: The dependent variable of upward wage mobility was computed from the difference between the monthly gross wage (in euros) of the current job in Austria and the last job outside of Austria. Wages were measured as ordered variable using more than 22 different wage brackets. Upward wage mobility means that a worker had to move up at least one wage bracket.⁹ The dependent variable upward class mobility is based on a class variable introduced by Featherman and Hauser (1978). Based on their work we use mobility between the 10 ISCO main groups as an indicator for class mobility.¹⁰ To calculate this variable, we compare the current job in Austria and the last job outside of Austria of the group under study. Our dependent variable changes of the occupational field is computed based on a comparison of the current and the last job categorized into 43 ISCO submajor groups, which are based on their similarity in terms of the skill level and skill specialization required for the jobs. In our analysis, the focus lies on commuters with no prior work experience in Austria.

Independent Variables

A number of key independent variables are used in our logistic regression analyses. Our models include sociodemographic indicators: gender (women = 1, men = 0), marital status (married or living in cohabitation = 1, all others = 0), and age (measured in years). We also captured the human capital of cross-border commuters (education, language command) and categorized the commuters’ education applying a collapsed, three-ladder International Standard Classification of Education (ISCED) scheme. We thus compare cross-border commuters with a university degree and those without formal training with a reference group of cross-border commuters with intermediate qualification. To scale our interviewees’ German-language skills, we use a variable on self-reported German proficiency ranging from 1 to 5 (“no knowledge at all” to “very good knowledge”) (van Tubergen and Kalmijn 2005). Based on this variable we calculated two binary variables (“German skills very good”; item 5), “German skills weak”; item 1 + item 2; the reference category for those variables is “German skills moderate”; item 3 + item 4.

To capture the social capital of cross-border commuters, our empirical analyses apply measurement concepts based on the Aguilera–Massey framework (2003). The near family tie variable measures the extent to which spouses, children, siblings, parents, and grandparents had at sometime gathered or were still gathering working experience in Austria. Within each of these kinship categories, our index adds one point whenever a family member was currently working in Austria and another if a family member had ever been working in this country, yielding an index range of 1 to 5. A similar index was calculated (ranging from 1 to 5) with the far family tie variable to measure social capital through relationships with other family members and relatives who were or had been working in Austria, that is, uncles/aunts, cousins, nephews/nieces, brothers/sisters/parents-in-law. The variable Job search via social network (Aguilera and Massey 2003) served to distinguish two different groups of cross-border commuters: first, commuters who had found their current main job in Austria through social networks, that is, the agency of family members, relatives, friends, and acquaintances; and second, those who had applied other job-finding methods, that is, individual applications, public or private recruitment agencies, entrance into self-employment, intrafirm job mobility by placing training positions, temporary or permanent relocation, promotion, or other methods. Finally, in regard to the relevance of social network characteristics, we use friendship with an Austrian (as a binary variable) to include a social capital indicator to account for any close connection (relationships based on trust) with locals.

Moreover, we include in our regression analyses a variable that measures the ethnic composition of companies in terms of the national belonging of workers. We used a concept developed by Falcón and Meléndez (2001) for the degree of ethnic workplace segregation and compare commuters who were employed in firms in which the majority of workers were coethnics with those who worked in firms composed of an ethnically diverse workforce. We also assessed the labor market characteristics of Centrope by distinguishing between the commuters’ industrial sector as to wholesale and retail trade, agriculture and forestry, construction, manufacturing, gastronomy and accommodation, health services/social work, other personal services, and business-related services. Empirically, we focus our discussion on those branches that have attracted a higher number of commuters during past years (construction, gastronomy/hotels, wholesale/trade, health industry and social work, other personal services). The reference category includes employment in all other branches (agriculture, manufacturing, business-related services). The analysis further includes a variable that captures the frequency of commuting (daily commuting vs. other forms of commuting) in order to control for specifics possibly connected with the respective type of commuting. In addition, our regression analyses control for the following variables: nationality, region/locality of the job, company size, wage based on collective bargaining, working time in Austria. Descriptive statistics for all independent and dependent variables can be found in the Appendix.

Sample

This study provides several interesting insights into the social composition of cross-border commuters in Centrope (see Table 2). Commuters in our study show mostly mid-level qualifications (ISCED 3–4: 77 percent). A relatively small proportion (9 percent) is qualified at a low level only, every seventh cross-border commuter is a university graduate. Thus, they proved significantly better qualified than ex-Yugoslav and Turkish-rooted migrants—traditionally the most important immigrant groups in Austria. However, cross-border commuters in our sample still show a lower qualification level than EU-15, EU-10 workers, and Austrian natives.

TABLE 2 Major Labor Market Characteristics of Natives and Migrants (“Foreign-Born”) in Austria, 2012

	Micro Census Data Austrian Labor Market (LFS)					TRANSLAB Data
	Natives	EU-15	EU-10	Ex-Yug.	Turkey	Commuter	Noncommuter
Education
ISCED 0–2	8.4	4.5	4.9	29.8	60.9	9.3	7.1
ISCED 3–4	70.3	53.5	67.3	64.2	35.1	76.9	72.5
ISCED 5–6	21.3	42.0	27.8	6.0	3.9	14.8	20.4
Industrial sector
Agriculture and forestry	5.0	1.1	1.4	0.4	0.2	2.8	2.8
Industry and commerce
Manufacturing	16.0	13.7	13.1	18.7	24.2	6.8	17.8
Construction	8.3	6.5	13.6	18.9	14.8	17.6	7.5
Other	1.5	0.5	1.0	0.7	0.7	1.0	2.5
Total	25.8	20.6	27.7	38.4	39.8	25.4	27.8
Services
Wholesale and retail trade	15.0	15.6	8.4	14.1	12.5	12.1	10.7
Gastronomy and accommodation	4.6	9.4	13.3	10.4	14.7	19.2	5.6
Health service, social work	9.7	9.4	11.8	8.9	4.7	17.4	6.8
Other, personal services	39.9	43.9	37.4	27.9	28.2	23.0	46.2
Total	69.2	78.3	70.9	61.3	60.1	71.8	69.3
Firm size
Small firm (≤10 employees)	33.5	33.0	42.7	27.4	28.6	26.9	22.8
Medium-size firm (11–49 employees)	27.5	22.7	26.4	30.5	25.6	52.6	41.0
Large firm (≥50 employees)	38.9	44.3	31.0	42.1	45.8	20.5	36.2
Gender
Female	44.9	48.8	51.6	44.5	30.9	42.2	45.5
Male	55.1	51.2	48.4	55.5	69.1	57.8	54.5
Age
21–35 years	32.5	35.6	36.2	32.4	38.9	47.8	38.4
36–50 years	45.0	45.2	42.3	49.4	50.5	37.7	38.8
51–65 years	22.5	19.2	21.5	18.3	10.5	14.5	22.8
N	60,652	2,529	1,230	3,352	1,372	1,347	1,340

Sources: Micro Census 2012, Labor Force Survey 2012; survey 2012/13; authors’ calculations; data include workers between 21 and 65 years old with a main job in Austria.

The majority of cross-border commuters in our study work in services (e.g., gastronomy: 19 percent; health: 17 percent) or in construction (18 percent), a comparatively small group works in agriculture or in manufacturing. Commuters in our sample set themselves clearly apart from natives, EU-10, and EU-15 migrants employed in Austria. Furthermore, commuters work mainly in medium-size firms (53 percent). This distinguishes them from all other labor market groups under investigation, who are clearly more frequently employed in large and small enterprises, as shown by the conducted statistical tests. Lastly, cross-border commuters in our study are relatively young: approximately 48 percent are younger than 35 and another 38 percent are between 35 and 50 years old. They are thus younger than any other comparison group. Only 14.5 percent of the cross-border commuters in the sample are older than 50.

These indicators point to the structure of a group that has so far received less attention on the part of transnational labor research in the EU. In the following we will examine the extent to which the commuters under study are able to improve their wages and occupational status through transnational labor activities.

OCCUPATIONAL MOBILITY: DO COMMUTERS IMPROVE THEIR STATUS?

Table 3 presents a descriptive overview of the patterns of upward mobility in the region under study. For this table, we calculated direct shifts between two jobs (the current job vs. the last job) and compared the wage, the class position, and the occupation of those two jobs of our cross-border commuters under study and a comparison group of workers who are employed in Slovakia, Hungary, or the Czech Republic. With the analysis of these three related but independent dimensions of occupational mobility we want to contribute to a better understanding of possible status changes that are linked with cross-border commuting.¹¹ Empirically, we observe a number of relevant findings:

TABLE 3 Wage and Class Mobility in the Central European Region

	Upward wage mobility	Job in CZ, HU; SL	Job in Austria	N
Total***	No	58.5%	41.5%	1,488
	Yes	39.1%	60.9%	1,199
	Upward class mobility	Job in CZ. HU; SL	Job in Austria	N
Total***	No	51.4%	48.6%	2,351
	Yes	39.3%	60.7%	336
	Change of occupation	Job in CZ. HU; SL	Job in Austria	N
Total	No	50.1%	49.9%	2,026
	Yes	49.2%	50.8%	661

Source: TRANSLAB survey 2012/13.

Notes: N = 2,687; authors’ calculations; data include workers between 21 and 65 years old with a main job (minimum 20 hours/week) in Austria; mobility is measured as direct job shifts; we selected percentages from cross-tab rows; +p < 0.1; *p < 0.05; **p < 0.01; ***p < 0.001; χ² test for upward wage mobility: 100.12, for upward class mobility: 17.02; for change of occupation: 38.51.

• First, there is much more upward wage mobility for cross-border commuters who work in Austria and less upward mobility for those who continue working in the Czech, Hungarian, and Slovakian regions bordering Austria (60.9 percent vs. 39.1 percent of all wage upward changes).

• Second, we also observe better chances to improve the class position for those workers who take up a job in Austria compared to those who continue to work in their home regions in Slovakia, Hungary, or the Czech Republic (60.7 percent vs. 39.3 percent of all class upward changes).

• Third, roughly 25 percent of the workers who changed their jobs in the Central European Region also experienced a change of occupational field. Interestingly, this form of mobility is equally likely for transnationally mobile workers as well as for noncommuters (50.8 percent vs. 49.2 percent of all changes of the occupational field).

• Fourth, overall there is much more upward wage mobility (N = 1,199) than upward class mobility (N = 336) or occupational change (N = 661) in the Central European Region. This is observable for the local/national labor market as well as for cross-border labor market mobility.

Using several logistic regression analyses, in a last step we analyse upward wage mobility, upward class mobility, and changes in the occupational field of cross-border commuters working in Austria. Table 4 includes a number of sociodemographic indicators, variables that capture the human capital of cross-border commuters (education/formal training, language command) and their social capital (near and far family ties, friendship with Austrians), the relevance of social networks for the job search in Austria as well as variables that cover the characteristics of firms and sectors in which cross-border commuters found employment.

TABLE 4 Logistic Regression for Upward Wage Mobility, Upward Class Mobility, and Changes in the Occupational Field in the Central European Region

	Upward wage mobility	Upward class mobility	Change in occupational field
	Exp(B)	Exp(B)	Exp(B)
Women (ref. men)	1.158	2.312***	1.683**
Married or partnership (ref. other household types)	1.315*	1.265	1.215
Age in years	1.021**	1.019*	1.020**
Without formal training (ref. intermediate qualification)	0.170***	0.466	0.570 (+)^(0.130)
University degree	1.042	0.978	0.761
German skills weak (ref. intermediate skills)	1.780*	1.004	1.261
German skills very good	0.665**	0.743	0.958
Near family ties	0.838+	1.179	1.122
Far family ties	1.102	1.111	1.052
Job search via social network (ref. other job search)	1.328*	1.215	1.282 (+)^(0.110)
Friendship with Austrians (ref. no Austrian friends)	1.584**	0.777	0.807
Workers in firm mainly own nationals (ref. other firms)	0.964	1.625*	1.436*
Construction (ref. manufacturing, agriculture, business services)	0.923	0.513*	0.579*
Gastronomy, hotels	0.728+	0.584*	0.644*
Wholesale, trade	0.668+	0.557+	0.754
Health industry and social work	1.075	0.422**	0.522*
Other, personal services	0.680+	0.708	0.803
Daily commuting (ref. other forms of commuting)	3.353***	0.546*	0.696
N	1,347	1,347	1,347
R^2	0.222	0.173	0.170

Source: TRANSLAB survey 2012/13.

Notes: Authors’ calculations; analysis includes workers between 20 and 65 years of age with a main job (minimum 20 hours/week) in Austria; regressions controls for the following variables: nationality, region/locality of the job, various other branches, size of firm, wage based on collective bargaining, working time in Austria; +p < 0.1 *p < 0.05, **p < 0.01, ***p < 0.001; regressions parameters: odds ratios ((Exp(B)), a positive effect of the odds ratios is expressed as e^β > 1 and a negative effect as e^β < 1.

Based on these regressions, we can show a number of relevant insights into the patterns of cross-border commuting: (1) The patterns of cross-border commuting differ substantially between men and women. Although, we cannot report significant differences between women and men regarding upward wage mobility, women still change their occupation more often than men. Female commuters also improve their class position twice as often as men. By and large, these findings are more in line with our “alternative” gender hypothesis since our data do not show—as one could have assumed from existing research—that men “automatically” have better chances to improve their status through cross-border commuting. (2) The marital status of cross-border commuters is relevant as well. Those living in a partnership/marriage have a better chance to improve their wages through a job shift to Austria. (3) The effects of age are also significant and indicate that increasing age correlates positively with upward mobility chances. Again, this corresponds with our “alternative” age hypothesis and confirms the assumption that specific work experience is needed on the highly segmented Austrian labor market. (4) Cross-border commuters without formal education have lower chances of realizing upward wage mobility through a new job on the Austrian labor market (reference group: workers with intermediate qualifications), which confirms our guiding research assumptions. Our findings also indicate that cross-border commuters without formal qualifications change their occupation less often. The regression analyses show no differences between the workers with intermediate qualifications and those with university degrees in this respect. Moreover, the effects of German-language command are surprising at first sight, considering the results of the extensive research on the positive effects of language skills for the integration of migrants (e.g., Dávila and Mora 2000; Esser 1982, 2006; Riederer and Verwiebe 2015; van Tubergen and Kalmijn 2005). Our findings show a higher probability of wage gains for those with limited German skills (who tend to work in manual jobs). One possible explanation could be that the relative wage position makes a difference, whether upward wage mobility occurs also depends on the position in the occupational hierarchy from which a cross-border commuter starts (upward wage mobility seems to occur more often in lower areas). However, this result needs further research in our view.

(5) Social capital and the use of social networks for job search are partially important for the explanation of the patterns of cross-border commuting in Centrope. There is a negative effect of near family ties on upward wage mobility, in accordance with the theoretical argument of authors like Granovetter (1974) and Aguilera and Massey (2003); this also confirms our assumptions from the theoretical part of the study. The analyses also show that friendships with Austrians improve wage upward mobility chances but have no further effects on the other two independent variables. With regard to our arguments in the theoretical section of the study, one could at least partially confirm the idea that social contacts and friendships with Austrian natives are beneficial for achievable wages since they represent bridges to the major ethnic group residing in the destination country. This form of social capital can broadly be referred to as “bridging social capital” (Putnam 2007). In addition, the use of social network contacts on the Austrian labor market is relevant. There is a significant positive effect on wage upward mobility and an increase in the likelihood of changes in the occupational field (although this effect is not highly significant). Finally, in terms of the relevance of social capital and social network, our analyses show that ethnic composition of the workplace matters as well. Those cross-border commuters employed in firms in which the majority of workers were coethnics have higher chances of upward class mobility and changes in their occupation (Falcón and Meléndez 2001). (6) We can show that the chance to increase the wage or class position through cross-border commuting in service branches like construction, gastronomy, trade, the health industry, and personal services are much lower than in other branches, confirming our theoretical assumptions. By the same token, employment in those branches is less often accompanied by a change of occupation for workers. Interestingly, these are the branches in which employment of cross-border commuters is important for the Austrian economy. (7) Finally, the frequency of commuting matters: As hypothesized, daily commuters have a much higher chance of improving their wages than workers who commute on a weekly or monthly schedule. However, this corresponds to lower chances of upward class mobility for daily cross-border commuters.

CONCLUSION

In this study we take a closer look at whether geographical mobility in the Central European Region is linked with occupational mobility. The study was designed to take Centrope as an example of a novel transnational European labor market reflecting the differentiation of movement patterns: from once-in-a-lifetime migration to mobility as a life strategy. Commuting here is a form of transnational mobility that results neither in definitely leaving one’s region of origin nor in definitely integrating in a host society, but in an intermediate process (Fassmann 2003: 438). Overall cross-border commuting can be perceived as a prototypic example of versatile postmodern life concepts, as both multilocal and mobile (Wille 2012).

It becomes clear that commuters settle into the dynamic of uneven experiences of occupational mobility as they start working in Austria. For example, in terms of upward wage mobility the young, the lowly educated, as well as commuters working in construction and various service branches are confronted with lower chances. On the other hand, there are significant wage benefits for daily commuters as well as cross-border commuters with Austrian friends. This indicates that the transnational labor market in the Central European Region is largely structured by social networks (Verwiebe et al. 2015; Wiesböck et al. 2016).

While the vast majority of cross-border commuters in our study show mid-level qualifications, the chance for some groups to improve their occupational status in Austria is lower than for other groups. This is particularly the case for men, younger workers, daily commuters, and workers in construction as well as several key service branches. The important question in this regard is whether this is an effect of disadvantage or it is “self-chosen,” meaning that commuters might chose economic gains over the recognition of their qualification because their status is mainly constituted through their financial power in the region of origin. Commuters are confronted with a dual frame of reference in their home region and receiving region in terms of wage level and purchasing power parity. For them it is possible to experience occupational downward mobility while at the same time facing upward wage mobility compared to the former job in the home region. In this context, we should pose the question: Is this a problem and, if so, for whom? In other words it would be crucial to delve deeply into the intentions behind mobility. If it is primarily earning more money and improving the status in the home region, occupational downward mobility does not constitute a downgrading factor for commuters themselves. Therefore, it would be of great interest to include the subjective scale of expectations and motivations for mobility.

Against the background of our results, in future studies it would be interesting to examine whether commuting leads to a convergence or divergence of average wages over time in particular branches in the Austrian border region. In addition, further research in this field could focus on the impacts of cross-border commuting on economic and social inequality in the home societies. Does labor commuting lead to decreasing economic imbalances between sending and receiving regions? Do new potentials for cooperation and conflict arise between the participants in this transnational labor market? As we can see, the scale of this debate is extensive and multifaceted even at the local level. Further research in this field would contribute to a better understanding of the dynamics and implications of the transnational labor market in Centrope.

Additional information

Notes on contributors

Laura Wiesböck

Laura Wiesböck is a Ph.D. candidate and researcher in the Department of Sociology at the University of Vienna. Her current research interests fall mainly in the field of social inequality and social transformation, migration, and transnational labor markets.

Roland Verwiebe

Roland Verwiebe is a professor of social stratification research and quantitative methods at the University of Vienna. His main research interests cover European integration, social inequality, migration, labor markets, attitudes, and values.

Notes

Various aspects of occupational mobility processes attached to migration can be studied, for example, downward, upward, and lateral mobility in terms of class position or wage, changes in the occupational field, changes of industry affiliations, the duration of unemployment phases after migration among others. In this study we focus on three aspects: upward wage mobility, upward class mobility and changes in the occupational field.

According to Horvath (2012), this gap is also due to the issue that transnational quantitative methodologies have so far received little attention, particularly regarding the important question of how to integrate quantitative methods into transnational research. Target populations as well as units of reference are still commonly defined in national terms, along the line that the “nation state society is the natural social and political form of the modern world” (Wimmer and Schiller 2003: 217).

The region consists of eight federal provinces, regions, and counties with a total population of approximately 6.5 million.

The A8 countries (Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, and Slovenia) joined the European Union during its 2004 enlargement.

The statistic includes dependently and independently employed people with citizenship of Hungary, Slovakia, or the Czech Republic registered on the Austrian labor market.

As a “conservative-corporatist state” (Esping-Anderson 1990), Austria has a highly developed social security system based on the idea of status preservation of wage earners (Obinger and Tálos 2009: 101). Labor market risks are strongly reduced by the country’s welfare system (Verwiebe et al. 2014a). Unemployment benefits are quite generous, especially in comparison to unemployment benefits in Hungary, the Czech Republic, and Slovakia, which established much more liberal welfare systems over the course of the past two decades (cf. Fábián et al. 2014; Guger et al. 2009; Kahanec et al. 2014; Lakner and Tausz 2016; Ripka and Mareš 2016).

It should be mentioned that poverty measures on the basis of household income fail to capture other important dimensions of poverty (living standard) and to reflect subjective perceptions of well-being (Greeley 1994).

We are aware that our quota sample creates some limitations with regard to representativity. However, it was the best available option to conduct our study since no systematic knowledge was available on the social composition of the population of cross-border commuters at the time of the survey.

Thus, we calculated 1,199 wage upward mobilities through cross-border commuting, 73 percent percent of those included mobility over two brackets and more.

The International Standard Classification of Occupations (ISCO) is a project of the International Labor Organization (ILO) and provides a system for classifying and aggregating occupational information.

A skilled worker from Slovakia (e.g., a trained toolmaker) could work in a better-paid position in Austria, while changing the occupational field and experiencing downward class mobility as an unskilled construction worker.

APPENDIX

TABLE A1 Descriptive Statistics for Cross-Border Commuters

	N	Min	Max	Mean	SD
Dependent variables
Wage upward mobility	1,347	0	1	0.54	0.50
Wage upward mobility	1,347	0	1	0.15	0.36
Change of occupation	1,347	0	1	0.25	0.43
Independent variables
Women	1,347	0	1	0.43	0.49
Married/cohabiting	1,347	0	1	0.63	0.48
Age in years	1,347	21.0	65.0	37.93	10.93
Low-level education (ISCED 0-2)	1,347	0	1	0.09	0.28
High-level education (ISCED 5-6)	1,347	0	1	0.14	0.35
German skills weak	1,347	0	1	0.08	0.27
German skills very good	1,347	0	1	0.32	0.47
Near family tie	1,347	1	5	0.40	0.64
Far family tie	1,347	1	5	0.40	0.70
Job finding via social network	1,347	0	1	0.63	0.48
Friendship with Austrian	1,347	0	1	0.71	0.45
Ethnic workplace segregation	1,347	0	1	0.22	0.41
Construction	1,347	0	1	0.17	0.37
Gastronomy	1,347	0	1	0.20	0.40
Wholesale and retail trade	1,347	0	1	0.11	0.32
Health services, social work	1,347	0	1	0.13	0.34
Other personal services	1,347	0	1	0.14	0.34
Daily cross-border commuting	1,347	0	1	0.58	0.49
Control variables
National origin: Slovakia	1,347	0	1	0.37	0.48
National origin: Hungary	1,347	0	1	0.41	0.49
Firm size (below 10 workers)	1,347	0	1	0.26	0.44
Wage based on collective bargaining	1,347	0	1	0.41	0.49
Full-time employment (35 hours plus)	1,347	0	1	0.75	0.43
Weekly commuting	1,347	0	1	0.31	0.46
Labor market region Vienna	1,347	0	1	0.53	0.50
Labor market region Vienna other Austria parts	1,347	0	1	0.06	0.24

Source: TRANSLAB survey 2012/13; authors’ calculations.