At the Origins of Migration Choices: A Survey of Students at Two South European Universities

Abstract

Migration research has long highlighted the role of factors influencing migration flows at the structural level. Recent literature has shifted researchers’ attention to the individual drivers influencing the definition of migration strategies and, before that, the individual propensity for mobility. In this paper, we present the results of a multiple regression model applied to data collected by means of an online survey of students at the universities of Salerno (Italy) and Extremadura (Spain). The model highlights the low prominence achieved by factors such as gender and parental cultural capital on this propensity. A more important role is played by the personal experience of living abroad, a proactive attitude toward the future, and the propensity to seek professional self-fulfillment even at the price of sacrificing one’s territorial affiliation.

1. Introduction

The debate on the drivers of migration has long revolved around the analysis of structural factors that migration scholars have always considered to be at the origins of population movements. Migration scholars have therefore long focused on variables such as employment and unemployment rates and wage levels in the areas of origin and destination of mobility flows. Recently, however, the increasing complexity of these flows in what has been called “the age of migration” [1] has pushed scholars towards broadening research perspectives on this issue. Increasing attention has been paid to factors and motives operating (1) at the meso level, such as the social networks to which potential migrants may have access, and (2) at the micro level, such as the personal experiences or personality types of the individual actors who move into the migration scene [2]. In addition, the role of non-economic drivers—such as those relating to the choice of a particular lifestyle [3,4]—may acquire more relevance when migration choices are made by people of higher social or cultural status. This does not mean that these factors do not also operate at lower levels in the social hierarchy [5], but it seems reasonable to assume that those belonging to the middle or upper classes have greater degrees of freedom in this regard.

The purpose of this work is to estimate of the relative impacts of some individual drivers, such as living abroad, proactive attitude, and professional self-fulfillment, along with gender and cultural capital, on individual mobility decisions and strategies. The study focuses on a specific segment of the population, that is, students enrolled in humanities or social science degree programs in the universities of Salerno (Italy) and Extremadura (Spain). Specifically, we employed the online survey technique to collect data on this target population, in order to investigate the life, study, and mobility experiences of these students, as well as their attitudes and opinions toward their personal and work future [6]. Next, we employed a portion of the collected data to construct a multiple regression model measuring the relative weight of several variables on the students’ propensity to emigrate once they finish their studies. The model shows the low weights of factors such as gender and family cultural capital in the development of this propensity. A more important role in these individual paths seems to be played by the familiarity already acquired with the experience of living abroad, the development of a proactive attitude toward the future, and, above all, the propensity to seek professional self-fulfillment even at the price of sacrificing one’s territorial belonging [2,7]. In the following pages, after a brief review of the literature on migration drivers (see below, Section 2), we will describe the context of our research, the methodology we employed, and the basic features of our sample (Section 3). Next, we will present the measures we used and the casual model we developed on this basis (Section 4). Finally, we present the research results we obtained, and we will try to show the relevance of our research work for current debates on the drivers of migration (Section 5). More specifically, our results seem to us in line with recent research on the topic, which may be indirect evidence of the integration of this segment of the youth population of two regions on the periphery of Europe (Campania and Extremadura) into the social and cultural climate of globalization.

2. Theoretical Framework

Drivers of Migration: A Synthesis of the Debate

Why do people move away from their homeland? This is the question at the heart of the international debate on the factors behind migration. Apparently, the starting point of this debate is the concept of imbalance between population and resources, borrowed from the theory of evolution [8]. Not surprisingly, therefore, in one of the first attempts to provide a general answer to this question, it has been pointed out that people basically migrate in search of better living conditions for themselves and for their loved ones or escaping situations of danger in their homeland [9] (see also [10]).

Lee’s theory still underpins the widespread view in migration studies, which frames the issue of mobility drivers in terms of so-called push and pull factors—basically viewed as systemic dynamics related to the demographic, economic, and political spheres [11]. The general theoretical framework in which the analysis of push and pull factors is framed is provided by (neo-classical) rational choice theory. Under this theoretical approach, individuals pursue “well-being” by always seeking the best possible personal fit with existing structural conditions. Such a theory suggests that “individual rational actors decide to migrate because a cost–benefit calculation leads them to expect a positive net return, usually monetary, from movement” [12] (p. 434).

However, subsequent migration research has shown that structural push and pull factors are not the only variables at stake when looking at the motivations behind the migration choices of individuals, couples, or even families, basically because of the underlying complexity of migration processes (see, for instance, [13,14]). In particular, migration systems theory has highlighted the existence of a plurality of links between territories of origin and destinations of migration and the role played by networks in fostering and directing flows [15,16]. Moreover, growing awareness of the roles played by human agency [17,18] and reflexivity [19] in shaping the social world in which we live has been generating various attempts to classify different types of migrant pathways and identities, often based on the reasons for migration (see, for instance, [20]). Therefore, these attempts focus on the different types of “push” or “pull” at play not only at the systemic or structural level but also at the meso level of social networks or at the micro level of individual aspirations, interactions, and experiences.

A recent review on this stream of literature [2] has listed 24 factors identified as drivers of migration, which can be aggregated into nine main dimensions—demographic, economic, environmental, human-development-related, individual, politico-institutional, security-related, socio-cultural, and supranational. In the concluding remarks, the authors note, following King [21], that much remains to be studied regarding the complexity of interactions among each factor, especially when considered from a dynamic perspective, which embraces the individual’s migration journey from the moment before the mobility choice to the different stages of its development (see also [7]). However, they maintain that economic drivers have received more attention than others, and that different social groups and categories may be differently affected by the identified factors, showing different degrees of freedom from them. This opens up a wide space for sociological research vocationally interested in capturing, with reference to different social groups or categories, the influence of some meso and micro factors in defining migration choices and implementing migration strategies, as well as in redefining by this route the links between micro, meso, and macro in the evolving field of migration studies [22].

In this debate, special attention can be paid to variables related to individual non-material resources. Personal dispositions may affect individual migratory choices. This can be true, for instance, as regards personal emotions and feelings toward one’s country or locality of origin, degree of open-mindedness, or the desire to have a better life or at least to live new experiences by going abroad [23,24,25]. Moreover, individual or even familial mobility experiences have often been seen as pivotal in the development of a positive attitude toward migration and, therefore, in migration choices. According to Czaika and Reinprecht [2], empirical research shows that “individuals who have migrated in the past or who have family members with migration experience are more likely to migrate in the future” [2] (p. 17) (see, for instance, [26]).

These findings inspired our research design, which focused on the individual drivers that might influence the propensity to migrate in a specific population, university students from peripheral areas of southern European countries.

Specifically, we tried to address the following research questions:

• RQ1: How much is the propensity to migrate of university students affected by drivers such as the personal experience of living abroad, the development of a proactive attitude toward the future, and the propensity to professional self-fulfillment?
• RQ2: How much do more structural (although non-economic) factors, such as gender and the family’s cultural capital, affect the propensity to migrate of university students?

3. Materials and Methods

3.1. A Survey of Students at the Universities of Extremadura and Salerno: Context and Research Design

Research questions were addressed by developing exploratory research based on a quantitative approach, specifically an online survey. The research unit of analysis is represented by students above 18 years of age enrolled in the years following their first year at the public universities of Salerno (Fisciano campus) and Extremadura (Cáceres campus). We chose these universities because they are large campuses located in two peripheral regions of southern Europe (Campania and Extremadura, respectively). Campania is part of the historic region of Mezzogiorno and is home to its historic capital, the city of Naples. However, the catchment area of the University of Salerno is limited to the provinces of Salerno and Avellino, which are characterized by an economic and social structure quite similar to that of Extremadura. The latter is a region in southwestern Spain, well known for having preserved its rural character in both the provinces of Caceres and Badajoz. More specifically, despite some important differences between them, these territories share many similarities, namely: “(1) a chronic condition of slow development and high unemployment, (2) a long history of emigration to the centers of national and global capitalism, (3) a foreign population still relatively small today” [27] (pp. 17–18). For instance, according to Eurostat data for 2019 (the year in which our research took place), the unemployment rate in the 20–64 age group was 6.6 percent in the EU-27, 13.8 percent in Spain, and 9.9 percent in Italy, but it was 21.2 percent in Extremadura and 19.9 percent in Campania. The second feature listed above is of particular relevance to our research, as migration from Southern European countries has increased significantly in the 21st century, especially since the 2008 crisis [28]. Moreover, this new emigration wave is mainly made up of young people with medium or high educational level and good training skills. This has resulted in a debate about the so-called “brain drain” that would be taking place from these countries, since immigration flows would by no means compensate for this loss of human resources [28,29,30].

From this perspective, we focused on university students, since they are the biggest beneficiaries of the investments made by families and educational institutions in terms of cultural capital, and therefore they could be part of highly skilled migration in the future. Much has been written recently about student mobility and its role in influencing subsequent migration choices [31,32,33,34,35]. For this reason, among the potential drivers of migration considered in our research, we placed particular emphasis on the mobility episodes already experienced by students, either directly or through the life paths of their closest relatives (indirect mobility).

More specifically, we decided to focus our research on undergraduate and postgraduate students in humanities and social sciences (including economics, management, marketing, and law), enrolled in the years after their first year. We decided to exclude from the research population students engaged in science or technology-based undergraduate and master’s degree programs. The growing literature on high-skilled migration, especially in these areas of the labor market and as regards intra-European migration (see, for instance, [36,37,38]), points out that a vast majority of career pathways for this type of graduate can be seen from an international (if not global) perspective. In contrast, the labor market for humanities and social sciences graduates may often be still conceived of as purely local. Therefore, we can assume that the propensity for territorial mobility is very high for tertiary students in science and technology. On the other side, we may still have doubts about students in other disciplines.

Data collection was performed between fall 2018 and spring 2019 by conducting an online survey. Our research design is based on experiences already carried out elsewhere in Italy [28] and can be summarized as follows. In the digital society, the way of doing social research is undergoing radical transformations [39]. Thanks to the diffusion of specifically designed software, the increase in researchers’ expertise, and the reduction of the digital divide, more and more surveys are conducted through the web [40,41].

The data collection tool was an online questionnaire. Unlike other quantitative social research techniques, this tool allows a significant reduction in the costs related to data collection and organization. Furthermore, it allows reaching a large population. Since this population consisted of students with extensive internet socialization, this tool seemed even more appropriate. As is well known, the price to be paid is the sacrifice of the statistical significance of the sample. In fact, the respondents were selected through a non-probabilistic sampling procedure, which implies that the research sample is not representative. However, this is in keeping with the exploratory nature of our research, which does not have the goal of extending the results to the entire Italian and/or Spanish student population.

Today there are many online platforms that support the construction and dissemination of web surveys—specifically, SurveyMonkey was our choice. Using this tool, the researcher is asked to choose from the available options, which have significant effects on the overall survey results (research results, response rates, dropouts, measurement errors, etc.) [42].

The research path [43] included, therefore, a first phase, in which we worked on a general review of the literature on the subject and purpose of our research. From this, we moved to the construction of a first draft of the online questionnaire. The second phase was dedicated to the construction of the applications and their translation into the languages of the target population (Italian and Spanish).

However, before proceeding to the ultimate version of the questionnaire, a brief pre-test was carried out with short qualitative interviews conducted with a small number of students. The results of this pre-test allowed a more valid and reliable construction of the stimuli that were presented to our target population. Once the questionnaire was defined, it was implemented online on the SurveyMonkey platform.

As we said before, in the third phase, we worked on the dissemination of the survey among both Italian and Spanish students (data collection). In order to do this, we asked colleagues who were teaching during the considered terms to allow the online survey to be promoted by a member of the research team. The fourth phase was devoted to the screening, processing, and statistical analysis of the collected data through the SPSS (version number 23) package. Descriptive and multidimensional statistics (factor and cluster analysis) were used for data analysis [6]1. Finally, we applied a multiple regression model in order to assess the relative incidence of some variables on students’ propensity to migrate in the future. In the next sections, we will show the features of this model and its results.

Before that, however, we feel it appropriate to outline some of the main characteristics of the sample formed by the 794 students who participated in the survey.

Table 1. Main sample features.

	Absolute Values			Percentage Values
	Cáceres	Fisciano	Total	Cáceres	Fisciano	Total
1. Gender
Men	135	115	250	17.0	14.5	31.5
Women	288	256	544	36.3	32.2	68.5
Total	423	371	794	53.3	46.7	100.0
2. Age
Less than 20	31	4	35	3.9	0.5	4.4
20-24	336	263	599	42.3	33.1	75.4
25-29	47	94	141	5.9	11.8	17.8
30 or more	9	10	19	1.1	1.3	2.4
Total	423	371	794	53.3	46.7	100.0
3. Educational qualification
High school	344	281	625	44.6	36.4	81.0
Technical, professional	69	78	147	8.9	10.1	19.0
Total	413	359	772	53.5	46.5	100.0
4. Cultural capital
High	133	84	217	18.3	11.5	29.8
Medium-High	105	111	216	14.4	15.2	29.7
Medium-Low	47	84	131	6.5	11.5	18.0
Low	85	79	164	11.7	10.9	22.5
Total	370	358	728	50.8	49.2	100.0

shows the data collected among respondents on some of these variables. As regards gender, 250 students identified themselves as men and 544 as women (we had no cases of other gender identifications). The preponderance of women in the sample is not surprising, since they make up a majority share of tertiary students in Italy [44] and they are about half of the student population in Spain [45]. It has long been known, moreover, that many degree programs in the fields of humanities and social sciences (literature, education, and sociology, to name just a few, but also marketing and law) attract mostly women today.

The age distribution of the interviewees is also in line with the expectations of the research team. In fact, Table 1 shows that more than three-quarters of the respondents (599 out of 794) are in the age group of 20 to 24 years old. However, a proportion of students lagging behind the ideal age group is also present.

In terms of the educational qualification that provided access to university, more than four-fifths of respondents (625 out of 772 valid responses) said they had a high school diploma. A minority, on the other hand, said they held a technical or vocational degree. This result is also in line with expectations, since students who have a technical or vocational qualification tend to enter the labor market without going through university.

A variable of particular interest in student condition surveys may be the social status of students’ families. In an attempt to identify a proxy for this variable, we constructed an index of parental cultural capital. We asked the respondents about their parents’ educational qualifications and constructed a classification dividing them according to four levels of family cultural capital: (1) high (both parents with a college degree); (2) medium-high (one parent with a college degree and one with secondary education); (3) medium-low (at least one parent with a secondary degree); and (4) low (no parent with secondary education). Table 1 shows that the distribution of students among these classes of cultural capital from the family of origin is relatively balanced, with a slight skew in favor of the two categories with higher cultural capital (433 out of 728 valid responses).

3.2. Measures

The drivers and the propensity to emigrate were operationalized through different procedures that took into account the specific nature of each measured construct (see

Table 2. Operational definition of the variables included in the multiple regression model.

Variable	Questions	Items	Measure
Propensity to emigrate	How likely do you think it is to go to work abroad once you finish your studies?	Extremely Unlikely Very Unlikely Somewhat Unlikely Neither Somewhat Likely Very Likely Extremely Likely	Seven-point Likert scale
Direct experience of migration	Have you ever participated in mobility programs during your studies (high school or university)?	Yes No	Dichotomous
	Have you ever lived for at least three months abroad?	Yes No	Dichotomous
Indirect experience of migration	Among the following people, is there anyone who has lived at least 1 year abroad? (multiple answers possible)	Parents (at least one) Grandparents (at least one) Close relatives (brothers and/or sisters) Other relative (uncles, aunts, cousins, etc.) Friends Other people	Multiresponse
Proactivity	For each couple of sentences, choose the one that best represents you	When I think about my future, I imagine it full of risks and unknowns When I think about my future, I imagine it full of possibilities and surprises	Dichotomous
		In life it is important to have goals and objectives It is useless to make so many plans because something always happens to prevent us from achieving them
		In life you have to take risks and pursue your dreams In life you have to be realistic and choose concrete goals
Job conditions sacrifice	What would you be willing to do to get a job?	I would accept an inadequate salary I would accept a job inconsistent with my degree I would accept a particularly hard job	Five-point Likert scale from strongly disagree to strongly agree.
Homeland sacrifice	What would you be willing to do to get a job?	I would change cities, staying in the same region I would change region I would change country, moving abroad

).

The propensity to emigrate is the dependent variable in the multiple regression model, and it was measured using a single question asking respondents how likely they are to move to a different country after completing their studies, on a seven-point Likert scale from extremely unlikely to extremely likely.

As regards the independent variables, they were operationalized as follows:

• Direct experience of migration was measured by combining respondents answers to two dichotomous questions: “Have you ever participated in mobility programs during your studies (high school or university)?” and “Have you ever lived for at least three months abroad?”;
• Indirect experience of migration was created by counting the number of “yes” responses a respondent gave to the items of the question “Among the following people, is there anyone who has lived at least 1 year abroad?” The higher the number of affirmative responses, the higher the indirect experience of migration.
• The operational definition of proactivity (that is, a mental attitude which tends to produce desired changes in the actor’s environment) is based on three pairs of opposing statements, for each of which respondents had to choose the statement that best represented them using a forced-choice technique. The proactive phrases were: “When I think about my future, I imagine it full of possibilities and surprises”, “In life it is important to have goals and objectives”, and “In life you have to be realistic and choose concrete goals”. The more respondents chose a proactive sentence, the higher their proactivity score. In other words, the idea behind this definition is that proactive people choose items that indicate an autonomous, non-fatalistic, and structured view of their existence.
• Job conditions sacrifice and homeland sacrifice were operationalized in the same way. Each variable is composed of three items for which respondents had to rate their degree on a five-point Likert scale from strongly disagree to strongly agree. Respondents’ scores on each variable were calculated by combining their responses, such that the higher the score, the higher their willingness to sacrifice.

4. Results

Students’ Propensity to Emigrate: A Multiple Regression Model

Research questions were addressed by means of a multiple regression model where the propensity to emigrate was used as a dependent variable, while proactivity, homeland sacrifice, job conditions sacrifice, direct migration experience, and indirect migration experience were defined as independent variables. Moreover, gender and parental cultural capital were further included as control variables (

Table 3. Multiple regression model (n = 722).

Model	Variables	B	Std. Error	Beta Standardized	t	Sig.	Tolerance	VIF
1	(Constant)	1.498	0.238		6.283	0
	Proactivity	0.154	0.045	0.125	3.443	0.001	0.992	1.009
	Homeland sacrifice	0.520	0.065	0.296	8.000	0.000	0.963	1.038
	Job conditions sacrifice	−0.096	0.054	−0.064	−1.767	0.078	0.997	1.003
	Direct experience	0.165	0.054	0.113	3.057	0.002	0.957	1.045
	Indirect experience	0.069	0.039	0.064	1.745	0.082	0.979	1.022
2	(Constant)	1.488	0.296		5.029	0.000
	Proactivity	0.154	0.045	0.125	3.433	0.001	0.988	1.012
	Homeland sacrifice	0.521	0.065	0.296	7.995	0.000	0.960	1.042
	Job conditions sacrifice	−0.097	0.055	−0.065	−1.762	0.079	0.983	1.017
	Direct experience	0.166	0.054	0.114	3.069	0.002	0.954	1.048
	Indirect experience	0.070	0.040	0.065	1.760	0.079	0.975	1.025
	Gender	0.028	0.080	0.013	0.345	0.731	0.978	1.022
	Parental cultural capital	−0.015	0.033	−0.017	−0.464	0.643	0.975	1.025

R square = 0.149; Adjusted R square = 0.142. Durbin–Watson = 1.847.

). Note that the multiple regression model included respondents who provided all answers to all relevant questions, namely 722 students.

The multiple regression model explains 14.2% of the variance of the independent variable; as the value of the adjusted R square (0.142) is very close to that of the R square (0.149), there is no significant loss of predictive power [46]. The Durbin–Watson value is between 1.5 and 2.5, so data should not be affected by linear autocorrelation ([47], p. 296); moreover, tolerance values above 0.1 and VIF values under 10 should guarantee that multicollinearity does not affect the data [46,47] (p. 309).

Results show that the variables homeland sacrifice (0.296), proactivity (0.125), and direct experience of migration (0.114) have a positive and statistically significant effect (p < 0.05) on migration propensity: the higher the values for the three abovementioned variables, the higher the propensity to migrate of the students. In contrast, indirect experience of migration and job conditions sacrifice do not have a significant impact on the dependent variable. The introduction in the multiple regression model of two control variables, gender and parental cultural capital, do not have any effect on the independent variables.

5. Discussion and Concluding Remarks

Digging deeper into the analysis of each research question, results show, regarding RQ1, that the propensity to emigrate is influenced by three specific drivers, linked, on the one hand, to the biographical path and the experiences of the students (direct experience of migration), and, on the other hand, to the individual attitudes of each respondent as regards their connection with their home territory and their perceptions of themselves as active subjects.

As regards RQ2, our findings show that some relevant sociodemographic features, such as gender and parental cultural capital, do not play a significant role in shaping the propensity to emigrate of our students, thus suggesting that some aspects related to the influence of close social networks have less impact on young people’s migration choices than in the past.

Our research results, therefore, confirm the findings already discussed in recent literature on migration pathways regarding the importance of individual predispositions and previous international mobility experiences in orienting young people toward international migration as a viable choice for their future (see Section 1 above). Apparently, open-mindedness, the desire to live new experiences, and the search for self-realization through what can be seen as a “good” job (even at the cost of moving away from home) are at the roots of the individual propensity to migrate, as well as the legacy of a temporary migration experience, especially those induced by European Union student mobility programs. Thus, even if there is no doubt about the fact that international differences in living conditions, growth rates, job conditions, and quality of life are among the main factors in migrations, it should be important to recognize that “the aspiration and desire to migrate is a crucial key factor that interacts with other external drivers of migration to build the final decision to actually migrate” [11] (p. 5).

The same can be said for the relative insignificance of variables such as gender and family cultural capital in influencing individual propensity to migrate. However, while there is broad recognition of the active role of women in recent waves of migration [21], a clarification must be made regarding the issue of parental cultural capital. Indeed, the population of university students in Fisciano and Caceres appears relatively homogeneous with regard to this issue, since it is well known that access to college education is not equally distributed among classes and social groups [48,49].

Our study has some unavoidable methodological limitations. First, it is well known that an online survey does not offer the possibility of working on a statistically representative sample. This necessarily makes the results obtained in this manner inconclusive—although incomplete or inaccurate information is better than no information at all. In our view, overcoming these limitations involves producing new social research. Specifically, it is possible to continue along the path of the online survey by expanding the cases under study—for example, by involving more universities in more European regions. Even more important would be to involve that part of the youth population that does not reach tertiary education, which most likely comes from the working class and the marginal strata of the societies under investigation. This change would significantly increase the sociological representativeness of research results obtained by this route.

Another methodological aspect that requires further attention and refinement concerns the measurement of the indirect migration experience variable. In the construction of the index, the degree of kinship/closeness was not taken into account; i.e., no weighting was adopted. In future research developments, we could adopt a weighting system that takes into account the degree of closeness to the respondent of those subjects who have migrated.

From the theoretical point of view, an issue that needs to be better considered is the link between university students’ propensity for mobility and the broader topic of youth mobility. The European Union’s international mobility programs, along with the availability of cheap transportation, the connection provided by social media, and, last but not least, the space without internal borders created by the process of European integration, certainly play a role in changing both the perception and the reality of migration among young Europeans [50]—including those from southern European countries. In fact, our research findings corroborate the idea that southern European youth (at least university students) now appear fully integrated into the social and cultural climate of globalization. However, this list can be further expanded by including other themes—for example, those related to youth tourism experiences.

Finally, individual propensity for mobility can be considered in relation to the life paths actually taken by young people. In this respect, a longitudinal panel study would be of interest, which would allow us to appreciate the extent to which young people’s intentions about the future are translated into individual plans and behaviors.