Dumitru Sandu, Paula Tufiș · Dumitru Sandu, Paula Tufiș *University of Bucharest 1 The text, with slight improvements, is part of the authors’ contribution to the WP4.1 (section

1

Spheres of Life in Youth Migration Processes:

a Multicountry and Multilevel Approach1

Dumitru Sandu*, Paula Tufiș*

*University of Bucharest

1 The text, with slight improvements, is part of the authors’ contribution to the WP4.1 (section of the

quantitative analysis) within the research report on determinants of the youth migration, written for the H2020 project YMOBILITY, Youth mobility: maximizing opportunities for individuals, labour markets and regions in Europe, 2015-2018, EC, Grant agreement ID: 649491

Citation: Sandu, D., Tufiș, P. 2018. Spheres of Life in Youth Migration Processes: a Multicountry and

Multilevel Approach, section in the research report of H2020 project YMOBILITY, Youth mobility:

maximizing opportunities for individuals, labour markets and regions in Europe, 2015-2018

2

Contents

Introduction ............................................................................................................................... 4

The first migration ..................................................................................................................... 7

Describing the motives ........................................................................................................... 7

Intensity .............................................................................................................................. 7

Typology ........................................................................................................................... 12

Determinants of the first migration ...................................................................................... 16

…by intensity of motivations ........................................................................................... 16

…by motivation types ...................................................................................................... 19

Return migration ...................................................................................................................... 23

Reasons to return .................................................................................................................. 23

Predicting reasons to return .................................................................................................. 28

Looking for information, channels and destinations................................................................ 30

How was the first migration “organised”: country and status differentials ......................... 30

Interactions between reasons and means for the first migration .......................................... 38

Means and determinants to reaching destinations by migration .......................................... 38

Instead of conclusions: from key problems to policy implications ......................................... 40

Annex on migration intentions................................................................................................. 48

3

Tables and Figures

Table 1.”What were your reasons when you first decided to migrate by yourself (rather than to

accompany your family)?” ...................................................................................................... 8 Table 2.Dimensions of first migration motivations: results of factor analysis for total YMOBILITY

sample ...................................................................................................................................... 9 Table 3.Motivation profiles for the first migration by countries .......................................................... 11 Table 4. Types of the first migration motivation by the intensity of the specific components ............. 13 Table 5.Typology of the first migration motivation by countries (%) .................................................. 14 Table 6.Predicting the self-assigned importance of the main reasons for the first migration ............... 16 Table 7.Predicting the types of motivation for the first migration ........................................................ 23 Table 8.Reducing the diversity of return motivations to four latent dimensions .................................. 24 Table 9.Types of return motivations by the intensity of the specific components ................................ 25 Table 10.Typology of the return motivations by residence countries (%) ............................................ 26 Table 11.Predicting the types of return migration motivations ............................................................. 29 Table 12.Channels for the first migration ............................................................................................. 31 Table 13.Distribution of the first migrants by channels and countries of origin................................... 31 Table 14.Predicting the adoption of a certain type of channel for temporary emigration .................... 33 Table 15.Predicting the adoption of a certain type of channel for temporary emigration by countries 37 Table 16.Main streams of first migrations by origins, destinations and channels ................................ 39 Table 17.People satisfied with their life by survey country and reasons to return (%) ......................... 40 Table 18.Return motivations by channels for the first emigration ........................................................ 41 Table 19.Life satisfaction by migration experience (%) ....................................................................... 42

Table A 1.How structured are intentions to migrate abroad in the next five years ................................ 48 Table A 2.` In any decision that you make about migrating or staying what is the importance of the

following reasons?` ............................................................................................................... 48 Table A 3.Intention motivations for high structured potential migration of those intending to leave in

the next five years .................................................................................................................. 49 Table A 4.Young Romanians on what are the important reasons for them to stay or migrate abroad, by

how structured are their intentions to leave the country (%) ................................................. 50

Figure 1.Two ways of representing dissimilarity among country profiles of first migration motivation

............................................................................................................................................... 10 Figure 2. Similarity networks among the motivation profile for the first migration ............................ 15 Figure 3.Clusters of country profiles function of reasons for returning home ...................................... 27 Figure 4.Networks of similarity among country profiles by proportions of youth into the seven types

of return motivations ............................................................................................................. 28 Figure 5. Summarising causal patterns of life satisfaction of returnees in for countries ....................... 41

Figure A 1. Similarity on motivations to migrate or stay, by countries: youth of high structured

intentions to migrate …………………………………………………………… ..49

4

Introduction2

The analysis in this section of the research report is devoted to answering questions related to

why and how youth are changing, in a selective way and temporarily, their usual residence

from one country to another3.

What can we learn from determinants, selectivity, and motivations of the youth migration

abroad that could be relevant for policies in the area? The exploration in this direction was

done by a large survey in nine European countries on about 30 thousands youth accomplished

in an international project on the European mobility of the youth (YMOBILITY) from nine

countries (King & Williams, 2018 Williams, Jephcote, Janta, & Li, 2018).

The basic frame to integrate information is that of the migration process. One can

define it by reference to internal components of migration actions or to external factors

structuring change processes associated to migratory movements. The first approach

works by considering the changings from unstructured desire to relocate, structured

intentions, preparatory actions, and successive acts of migration (first emigration, first return,

circular movements). This approach could underline, function of the specific objectives of the

research, a) the stages from unstructured desires, structured plans, to the first migration

behaviour, b) succession of different migration behaviours (structured intentions, first

migration, first return at home or circular migration) or c) balanced approaches of successive

intentions and behaviours. The YMOBILITY data that we used allow for an approach that is

closer to the second type of analysis: the stages in the subjective process at individual level

are not so much specified and the change is given by passing from the first migration, return,

circular migration and intentions to migrate function of previous migration experiences

(Sandu, Toth, & Tudor, 2018).

The external frame of reference for the migration process is based on considering the impact

of life course (Clark, 2013), urbanisation (Michael Beenstock, Professor Jordi Suriñach, &

Royuela, 2015) or modernisation (Zelinsky, 1971) changes on migration. This second

2 Dumitru Sandu

3 A large number of items into the questionnaire associated to this YMOBILITY project do not ask about specific

directions of migration – in or out of European Union - as the key interest is on why emigration or why returning and not so much on why some destinations. This is the reason we adopt, as in the EUROSTAT metadata, the term of migration. Majority of the residence changes recorded for the nine countries that are part of this project are within EU and could be referred as intra-EU mobility. The poor degree of specification of destinations for all migration related questions set the option for using the more general term of migration. And, else, all the residence changes that are referred to here are migrations according to the standard meaning of the term in demography.

5

perspective is included into our analysis by the series of predictors related to civil status,

education and job situation at individual level, or by regional characteristics that are relevant

for economic development (GDP per capita), social development (life expectancy at birth) or

population density (relevant for density of social networks and urbanisation degree).

The migration process is captured here, mainly, at the individual level by a typology making

the distinction among stayers, high probability migrants, one-time returnees, returnees on the

move (with intentions to migrate), circular migrants, and circular migrants on the move

(Sandu et al., 2018). The focus on migration motivations and typologies are key options to

keeping our approach as close as possible to agent-based model of migration (Klabunde,

2016). Motivation typologies are structured by combining 17 indicators referring to the main

spheres of life. These are related, mainly to job, human capital (education, health, lifestypes)

personal communities (family, housing, other relatives, friends), residential places (local

communities, regions, countries, natural amenities) (Sandu et al., 2018).

Descriptive and explanatory patterns for the whole sample of youth or by countries are

introduced in relation with the first emigration, return migration, intentions to migrate abroad

or by types of migration experiences.

Dissatisfaction and opportunities in different spheres of life are the key variables explaining

the dynamics within the nexus migration experience and motivation. This is contextualized

by factors related to person, community, region, and country, in a multilevel perspective. The

structures of the past and current motivations of migration for youth are addressed via a

comparative European level analysis.

A second basic series of patterns that are analysed refer to a typology of migration channels

by countries and inter-countries streams of migration.

The key methodological values of this analysis from `Youth mobility: maximizing

opportunities for individuals, labour markets and regions in Europe` (YMOBILITY) are

related to the fact that this is a multi-country comparative approach that is focused not

only on some sequences of migration but on the whole chain of the migration process from

the first migration, the first return, and possible repeated ones . For all these sequences we

considered four transversal dimensions of the process – intentions, motivations, self-

assessment of migration motivations and behavioural dimensions of accumulating resources

for migration or the change of residence per se .

6

Focusing more on the process rather than on its sequences (see table below) provides a better

frame for contextualising strategies or policies for a better linkage between migration (M)

and development at origin and destination, for optimising migration for its agents and

beneficiaries (migrants, their families, origin and destination countries and communities).

The migration process as a major conceptual frame of analysis

Pre-first

migration

First

migration

(M1)

First return

(M2)

Second

emigration (M3)

Second return (M4) or more

(circular migration)

Self-assessment •stayers

of M. motivations,

experiences and

consequences •high probability potential

migrants

•one-time returnees

•circular migrants

•on time returnees on the

move

•circular migrants on the move

Accumulating

resources for M •path dependency of

motivations at different stages

Change of

usual/permanent

residence

•M channels and motivations

Stages of the migration (M) processDimensions of the M process

information, channels, accumulating human, material , network or cultural capital

Types of M experiences or

relations among M stages or

dimensions

beh

avio

ura

l

dim

ensi

on

sSu

bje

ctiv

e d

imen

sio

ns

M. intentions accomplished/failed intentions; announces/reconstituted intentions

Evaluation of M per se for own case, or for others; hierarcy of importance for migration

motivation; structured or unstructured ideologies, perceived risks. These are

functioning as guids in migration/stability decision making. They are personal and social

at the same time.

motivations declared before M event or reconstituted after M; actual or reconstituted

reasons: specification by spheres of life (job, human capytal. Personal communities and

residential places)

M. motivation

Theoretical and methodological accents are better specified in a published article, derived

from this research (Sandu et al. 2018).

The conclusions chapter will introduce key families of findings in close connection with their

practical relevance for strategies and policies on optimising the nexus migration and

development.

7

The first migration4

Describing the motives

Intensity

The hierarchy of reasons to migrate is highly different between youth from New and Old

member states of the EU (Table 1). Higher salaries abroad, precarious jobs at home and

reaching a better quality of life are the specific reasons to migrate for the New Member States

(NMS) youth. The specificity of the motivation for the first temporary emigration for youth

from EU15 relies more on cultural factors related to style of life and education. Emigrating to

study for a degree is mentioned by 54% of the Old EU youth compared to only 32% in

Latvia, Romania and Slovakia. The finding is consistent with the third hypothesis.

4Dumitru Sandu, Paula Tufis. The analysis for this chapter differs considerably from the one proposed for

publication in Population, Space and Place journal (D. Sandu, G. Toth, E. Tudor - The Nexus of Motivation-Experience in the Migration Process of Young Romanians). This second version of analysis is developed under the idea of sensitivity analysis (Treiman, 2014), testing if changes in the data processing techniques bring about significant changes in findings. In the first version of the analyses we employed dummy motivation variables. Here, for the second version we worked mainly with the original five points scales that were used in the questionnaire for data collection (1 not at all important reason … 5 very important reason). In spite of the changes in the measurement procedure for the input variables of motivation, the results are highly consistent. The details for this evaluation are given into the body of the text. The first analysis worked with 13 motivation items and the second one with 15 items (adding climate and personal reasons to the list of analysed motivation indices).

8

Table 1.”What were your reasons when you first decided to migrate

by yourself (rather than to accompany your family)?”

Reasons for the first migration New Member

States

UE15 Total

To improve my language skills 67 71 70

To acquire new job skills 69 68 69

Lifestyle or culture 53 64 62

Career advancement opportunity 64 62 63

Higher salaries than home 80 59 64

General welfare (quality of life) 66 56 58

To study for a degree 32 54 50

To study as an exchange student 30 51 47

Precarious job 57 44 47

To join my family 44 43 43

Escape personal problems 36 42 41

Climate 36 41 40

To join friends 38 37 37

Housing opportunities 40 37 38

Healthcare 39 36 36

A factor analysis of the 17 reasons for the first migration reduced them to five latent

dimensions related to job, personal communities, education, lifestyle and personal problems

(Table 2). This motivational configuration is to a high degree consistent with the theoretical

model (Sandu et all. 2017). The job dimension of the motivation is first of all defined by

reasons related to higher salaries than at home and better career advancement opportunities.

The third item as relevance for these latent dimensions of motivation for migration is the

perception of the job at home as being a precarious one. Hopes for getting new job skills and

a better quality of life are also defining for the same latent dimension of motivation.

The second dimension as importance in the motivation matrix refers to personal communities

(Pahl, 2004) of the former migrant. Its key indicator is the reason of migrating to join

family/a partner/a spouse that were already abroad. Having friends abroad also defines this

family of motivations. Housing opportunities reasons are empirically included into the same

pack of reasons associated, mainly, with having relatives abroad. Healthcare reasons are

rather unexpectedly included into the same factor. Theoretically they are part of human

capital indicators, together with education and lifestyles. This suggests the interpretation that

health problems are perceived in close connection with the location and situation of the

Data source:

YMOBILITY

survey 2015.

Figures are

percentages of the

youth that had at

least one

migration abroad

and declared it as

important for their

decision to go

abroad. Rectangles

mark the high

profile values for

NMS or EU15

9

family. Education, personal problems and lifestyle are the other three dimensions of

motivation. They have a similar importance.

The importance of each of the five dimensions of first migration motivation is different by

country, residence, employment, human capital and demographics (age, gender, civil status).

The territorial patterns of the first migration are well structured by country and rural-urban

residence (Table 2).

Table 2.Dimensions of first migration motivations:

results of factor analysis for total YMOBILITY sample

JOB PERSONAL

COMMUNITY

(networks)

EDUCATION PERSONAL

PROBLEMS

LIFESTYLE

Higher salaries than home .829 .266 .007 .006 .040

Better career advancement opportunity .815 .108 .153 .020 .144

Previously unemploed in a precarious job .727 .129 -.036 .343 -.151

To acquire new job skills .634 -.107 .339 .045 .368

General welfare & quality of life .503 .425 -.067 .086 .487

To join my family / partner/spouse .081 .790 .218 .118 -.025

Healthcare .302 .647 .082 .280 .305

To join friends .074 .644 .253 .375 .156

Housing opportunities .281 .552 .177 .460 .108

To study as an exchange student .035 .204 .837 .166 .082

To study for a degree .096 .335 .781 .053 .046

Escape personal problems .111 .201 .127 .821 .053

Climate .047 .283 .096 .680 .342

Lifestyle/culture .018 .210 .106 .193 .817

To improve my language skills .349 -.224 .483 .159 .486

Rotation Sums of Squared Loadings (% of

variance)

19.104 16.150 12.509 12.004 10.531

Reasons for the first emigration Latent dimensions of motivations

Data source: YMOBILITY survey 2015. Technical details for the factor analysis: PCA, Varimax with Kaiser Normalization.

Input variables scaled on five points scales. N=4073. Weghted data. KMO=0.881. Predetermined number of factors

started from the theoretical model from figure 1. Figures in th etable are loadings resulted from rotated component matrix.

Rotation converged in 7 iterations. Cumulative eigenvalues 70%.

The nine countries are rather heterogeneous in regards to first migration motivation profiles.

They group into five clusters, according to their similarity in motivation profiles given by the

five latent dimensions (Fig 1A): Latvia-Slovakia, Romania-Ireland, United Kingdom,

Germany-Sweden, Italy-Spain. The grouping is sensitive to the way the motivation profiles

are defined. The input data for the dendrogram in figure 1B are the 17 indicators of the

mentioned motivations for the first migration recoded as dummy variables (1 for important

and very important and 0 for no importance, low importance and medium importance). In

spite of difference in input data, the two dendrograms are rather similar. The striking

difference is related to Ireland and Romania. Figure 1A puts them in the same cluster. Figure

1B separates them showing Romania to be more similar to Latvia and Slovakia and Ireland to

UK. What is the most reliable representation? Apparently, using external criteria, one could

consider that it is normal having Romania in the same cluster with Slovakia and Latvia, all of

10

them being former communist countries, New Member States of the EU, placed in the same

Central and Eastern European region. Similarly, historical paths would justify to a higher

degree having Ireland and UK in the same cluster of similarity. Such a conclusion would be

valid if the 17 indicators that were used as inputs for the dendrogram in figure 1B would be

distributed rather equally on the theoretical dimensions of motivation for migration. Another

factor that plays into the decision is the discrimination power of the items that were used for

classifications. Dummy variables that were employed for clustering in figure 1B are poorer

measures than the factor scores (continuous variables) involved in the clustering for fig 1A.

These are the reasons the validity of the classification is higher for fig. 1A compared to 1B.

A. Dendrogram using five factor scores as

described in Table 2

B. Dendrogram using 17 motivation

indicators recoded as dummy variables

(Sandu et al 2018) Data source: YMOBILITY survey 2015. Both dendrograms are generated through cluster analysis:

Pearson’s correlation z-scores were grouped by the furthest neighbour (complete linkage)

hierarchical clustering algorithm. The higher the similarity between the migration profiles of two

countries, the closer to zero on the dissimilarity scale the line joining the two countries is. Example:

Latvia and Slovakia are the most similar countries according their profiles for the first migration of

the youth (in figure A).

Figure 1.Two ways of representing dissimilarity among country profiles of first migration

motivation

11

Table 3.Motivation profiles for the first migration by countries

job personal

communities

(networks)

personal

problems

(residual

reasons)

lifestyle

and culture

education

Latvia 56 51 51 47 45

Slovakia 55 50 49 48 46

Romania 53 54 46 54 44

Ireland 50 52 48 50 49

UK 51 53 50 50 51

Italy 53 48 51 51 53

Spain 50 49 48 47 53

Germany 44 46 53 54 52

Sweden 42 48 52 49 51

Average intensity for the first migration motivation as

related to..

Residence

country of

the youth

Table 3 makes explicit the motivation profiles for the youth in the nine surveyed countries.

Here one can see why or through what criteria pairs of countries are similar. Latvia and

Slovakia are similar through the high propensity of their youth in adopting job motivation as

a reason for the first migration and through the very low propensity to migrate for lifestyle

and education reasons. This is one of the best-structured pairs of countries through their

similarity of migration profiles. The second group of countries of high similarity between

their migration profiles is formed by Germany and Sweden . Here migration is highly

individualised, with high shares of youth migrating for personal reasons. The intensity of

migration for job and personal community reasons is very low. There is an inconsistency

between the two country profiles here that is given by the fact that the German youth are

significantly more oriented towards lifestyle and education migration compared to the youth

from Sweden.

The youth from the South, from Italy and Spain, are marked by a high propensity to migrate

for education reasons and by the very low propensity to migrate for family and friendship

reasons (personal communities). Otherwise, Italian youth are delimited within this group by

the fact that there are also significant segments of youth with high propensities to migrate for

lifestyle and job reasons. The Spanish youth are individualised in comparison with the Italian

Data source: YMOBILITY survey 2015. Figures are averages by country and dimension of motivation for the first migration (factor scores converted in Hull scores to have a variation between approximately 0 and 100). Cells of the table in rectangles mark situations of a significant positive correlation (p≤0.05) between residing in the reference country and the intensity of the motivation for the first migration by the criterion on the column. Significant negative correlations are marked by italics. Example: the average intensity of job motivation for the Slovakian youth is 55. The relation between living in this country and having done the first migration abroad for job reasons is a positive and significant one for the significance level of 0.05.

12

youth by the fact that lifestyle, personal community and personal reasons are reasons to

migrate with a significantly lower probability among this group.

The youth from Romania and Ireland are highly similar through their high propensity to

migrate on the grounds of their personal community reasons and by their low propensity of

moving on the basis of personal reasons and education. This is a migration profile that is the

opposite of the profile of youth from Germany and Sweden, as described above. There are

also high dissimilarities between the profiles of the youth in the two countries: the youth from

Romania have a high propensity to migrate for lifestyle reason (as in the cases of the German

and the Italian youth).

The motivation profile of the youth from the UK is highly specific in the context of the other

eight surveyed countries, since the intensities of youth motivation for the first migration as

related to job, lifestyle and personal motivations are all very similar, on average. It is only

education and personal community reasons that are higher than the average for the nine

countries. Youth migration from the UK is similar to youth migration from Ireland and

Romania only under the aspect of the high propensity of migrating for family and friendship

reasons.

The motivations for the first migration differ not only through their intensity on different

dimensions. People do not take decisions by relying on just one or another dimensions of

motivation. They act function of different combinations of these motives or dimensions of

motivation structured into specific configurations or types. The identification of these types is

the purpose of the next subchapter.

Typology

The five dimensions of the first migration motivation combine into a set of eight types (Table

4). Some of them are simply types structured on specific dimensions. This is the case, for

example, of “the mainly job motivation” where job motivation per se has an average of 63

(on a scale with an amplitude of variation between 0 and 100, approximately) and much

lower average values on the other four dimensions.

There are three non-cumulative types of motivation each of them focused on a specific

dimension (mainly on job, mainly on education and mainly on networks). Three other types

are mainly combinations of two dimensions of motivation: lifestyle and job, personal

community and lifestyle and escaping personal problems plus getting a specific lifestyle. Two

13

motivations of cumulative type are structured in term of their intensity: cumulative upper

middle motivation and cumulative middle level of motivation.

Table 4. Types of the first migration motivation by the intensity of the specific components

job education networks lifestyle personal

problemsmainly job 63 43 40 27 44

mainly education 40 69 37 50 42

mainly networks 27 37 59 41 44

lifestyle and job 59 31 43 64 40

networks and lifestyle 56 54 59 59 38

escaping personal problems and getting

new lifestyles41 38 30 59 67

cumulative upper midle motivation 53 56 63 51 66

cumulative middle level motivation 52 52 51 48 53

50 50 50 50 50Total

Typologie of motivation Intensity of motivation of migration by dimensions

one-

dimensional

bi-

dimensional

(lifestyle

related)

multi-

dimensional

Data source: YMOBILITY survey 2015. The typology of motivation resulted from a k-

means cluster analysis with predetermined cluster centres. The values in the table are means

on intensity of motivation for the specific cluster and dimension. Example: the average

intensity of education motivation for the migrants in the category of mainly job motivated is

of 43.

The highest concentration of youth (Table 5) is in the category of cumulative middle level

motivation (27%) and upper middle level motivation (15%). Percentages on the whole

sample are not so relevant as country subsamples are very different by countries. What counts

for interpretations are the patterns of motivation by each country and the similarities among

these patterns?

In spite of the fact that, as expected, the diversity of the motivation patterns is higher in Table

5 compared to Table 3, the similarity pairs of countries are the same with Germany-Sweden,

Italy-Spain, Latvia-Slovakia and Romania-Ireland. The youth from Germany and Sweden are

very similar through the fact that their first migration was significantly under the impact of

personal problems motivation (beyond job, education, lifestyle or networks). Youth from

Italy and Spain continue to be highly similar in this new refined typology through their high

educational motivation for migration. Latvia and Slovakia continue to be highly similar

through their high motivation for migration for job reasons. More than that, in the new

classification these two countries are also similar through their high cumulative motivation to

migrate on the ground of lifestyles and job purposes. Romania and Ireland are also the most

similar two countries within the YMOBILITY sample. The reason for the similarity is

different from what Table 3 indicated in the simplified analysis on intensity of independent

motivations. The grounds for this similarity are no longer related specially to networks as a

14

means to migrate. The two countries are especially similar through youth migration as a

function of cumulated lifestyle and job reasons.

Table 5.Typology of the first migration motivation by countries (%)

Germany Sweden Italy Spain UK Latvia Slovakia Romania Ireland Total

mainly job 4 4 6 12 9 16 17 8 7 9

mainly education 10 19 18 16 9 3 4 4 11 11

mainly networks 9 14 4 9 9 7 9 10 9 9

lifestyle and job 3 4 9 6 7 11 11 15 13 8

networks and lifestyle 8 6 13 12 16 16 16 27 14 14

escaping personal problems and getting

new lifestyles

18 12 7 5 6 3 6 4 5 8

cumulative upper midle motivation 13 17 22 13 21 11 10 11 13 15

cumulative middle level motivation 36 24 21 27 23 33 26 21 28 27

Total % 100 100 100 100 100 100 100 100 100 100

N 528 368 482 569 833 332 337 227 402 4078

one-

dimensional

bi-dimensional

(lifestyle

related)

multi-

dimensional

Types of the first migration motivation

Data source: YMOBILITY survey 2015. Figures in the table are percentages out of the

total first migrants in the country in the reference category on the row. Highlighted cells

are for the significant associations between column and row values (adjusted standardised

residuals, not shown in the table). Example: 36% out of the total youth that returned home

to Germany are in the category of cumulative middle level motivation for their first

migration. This is a specific, significant motivation for the German youth first migration.

A new pair of similarity appears, surprisingly, between UK and Romania. These are two very

different countries. What makes them similar in this context is the fact that in both cases

networks and lifestyles as cumulated reasons to migrate are significantly higher than in the

other compared countries. The similarity should be explored to be better understood. One

could formulate an explanatory hypothesis in this stage. The striking similarity could emerge

between two very different streams of migration: climate migration from UK to Spain and the

stream of young Romanians going to friends and relatives abroad not only to accompany

them but also for a change in their style of life. In the first case the British stream is to escape

the UK climate for the sunny environment of Spain and for Romanians is mostly to escape

the poor level of living at home. Another significant change brought by the typology refers to

lifestyle motivations. These are no more an independent type. They appear only in

association with motivations for job, networks and personal problems.

The most accurate picture of the similarity networks among the nine countries according to

their motivation profiles for the first migration is presented in figure 2. Here one can see that

Germany and Sweden are similar but at a rather low level. Romania is similar to Ireland and

Slovakia, as marked by the figures from Table 5 but their degree of similarity is rather low

(r=0.45). The new lens of the similarity network shows clearly that the youth from Spain has

15

the most dissimilar profile and is the most specific one in the YMOBILITY context of

comparisons.

The youth segments having the most similar profiles with an abstract average profile for all

the countries are those from Germany, Sweden, and, to a lower degree, for the case of Spain.

In fact, the whole picture is that of a set of countries with rather high specificity in migration

motivation. It is only Latvia and Slovakia that are similar in their motivation profile of first

migration at a high level. For the rest of the countries, all the other similarities are at lower

levels and on very specific dimensions. The finding suggests that only national levels of

analysis for migration motivations are rather inappropriate and should be complemented with

regional and individual ones. It is what the multivariate analysis from the next section

indicates.

correlations

between

profiles

0.80 Latvia Slovakia

0.75 Germany

average profile

9 countries

0.70 Sweden

0.65

0.60

0.55 Italy UK

0.50

0.45 Irealand Romania

0.4 first order correlations

second order correlations

0.35

0.3

0.25 Spain

Figure 2. Similarity networks among the motivation profile for the first migration

Data source: YMOBILITY survey 2015.The profile of each country is determined by the share of the youth into one of the eight types of motivation (Table 5). A reference profile is constituted by the shares at the total level, for all the nine countries. The similarity among profiles is measured by Bravais-Pearson correlation coefficients. The higher the correlation, the more similar the motivation profile of the reference unit of analysis. Figure 2 presents a single linkage cluster analysis (Sandu, 1988). The degree of similarity between two profiles is given by the position of the horizontal lines connecting them by reference to left side scale or by the lower head of a non-horizontal segment connecting the units of classification. Example: the motivation profile of the youth from Slovakia has maximum similarity with the youth from Latvia (at the level 0.75). Romania is similar to Ireland and Slovakia at a rather low level of 0.45.

16

Determinants of the first migration

…by intensity of motivations

The picture from Table 3 (of similarities among motivation profiles of the nine countries) is

based on the measurement of bivariate relationships (correlation coefficients). This is the

reason some of its nuances could be misleading because their configurations could result

from third factors (age, gender, education etc.) that are different from country per se and

motivation dimension. This is why it is necessary to estimate the net effects of the residence

country keeping under control demographics giving the composition of the population. It is

what Table 6 presents with the series of five multiple regressions having dimensions of

motivation as dependent variables and 21 predictors that are related to age, gender, civil

status, education, residence community, occupation, residence country and NUTS2

development. The relations that continue to be of significant intensity in this multivariate

analysis Table are the “strong” ones that could be the grounds for interpretation of the social

context of the first migration of the youth.

Table 6.Predicting the self-assigned importance of the main reasons for the first migration

coeff. sig. coeff. sig. coeff. sig. coeff. sig. coeff. sig.

women -1.093 0.054 -1.265 0.035 -0.252 0.667 1.5687 0.012 -0.920 0.140

age 0.135 0.027 -0.127 0.038 -0.345 0.000 -0.039 0.593 -0.019 0.764

single, without children* 0.600 0.382 -4.479 0.000 -1.616 0.009 2.2234 0.001 -0.171 0.815

tertiarry education* 0.205 0.739 -1.236 0.075 3.456 0.000 0.9773 0.135 -1.533 0.024

secondary education* 0.702 0.521 -0.968 0.297 -2.143 0.024 -0.466 0.632 -0.368 0.704

manual occupation* 0.676 0.519 0.498 0.571 0.441 0.508 0.7637 0.384 0.847 0.321

administrative occupation* -2.865 0.011 3.071 0.005 0.169 0.877 0.8095 0.383 -0.100 0.922

student* -3.102 0.000 -1.709 0.037 7.643 0.000 0.6324 0.415 -3.816 0.000

large town* 1.564 0.087 -1.192 0.243 1.856 0.080 1.5503 0.146 0.609 0.507

small town* 0.608 0.521 -0.869 0.405 1.179 0.229 1.8754 0.087 0.232 0.789

Latvia* 4.265 0.019 -2.523 0.089 -3.007 0.024 -0.612 0.743 -0.179 0.895

Slovakia* 3.000 0.104 -1.339 0.274 -1.785 0.187 -0.367 0.814 -1.466 0.296

Romania* 0.676 0.715 2.048 0.225 -3.389 0.103 5.5074 0.004 -4.935 0.003

Ireland* -0.096 0.960 0.340 0.884 -1.053 0.459 2.9875 0.12 -1.957 0.306

Italy* 2.050 0.032 -4.618 0.000 2.166 0.050 2.3897 0.019 0.564 0.598

Spain* 0.147 0.884 -4.084 0.000 1.977 0.033 -1.28 0.392 -1.228 0.210

Germany* -5.350 0.000 -5.435 0.000 -1.720 0.072 4.364 0.000 4.698 0.000

Sweden* -7.981 0.000 -6.063 0.004 -1.151 0.474 0.482 0.695 1.832 0.098

GDP per capita NUTS2

2015 (ln) -0.372 0.662 0.764 0.367 0.294 0.794 0.0124 0.989 -0.128 0.854

Density NUTS2 2015 (ln) 0.187 0.554 -0.061 0.864 -0.053 0.857 0.5 0.148 -0.015 0.964

life expectancy at birth,

NUTS2 2015 -1.604 0.465 -2.134 0.373 -0.659 0.678 -2.986 0.145 0.727 0.734

constante 55.169 0.000 68.188 0.000 57.252 0.000 55.504 0 50.201 0.000

R2 0.103 0.073 0.181 0.044 0.048

N 4691 4691 4691 4691 4691

predictors Dependent variable= motivation related to...

job networks education lifestyle personal

problems

17

Note: OLS regression having as dependent variables the factor scores (converted as Hull

scores=50+14*factor score) for the motivation of the first migrations of the youth in the 9

YMOBILITY survey countries. Weighted data. Robust standard errors computed through

the cluster command in STATA, using NUTS3 reference. * Dummy variables. Reference

category for country of residence – United Kingdom. The decision on the reference

category is derived from the fact that UK is a highly specific country in terms of first

migration motivation according to figure 1A. For an easier reading of the table,

coefficients that are significant (p≤0.05) are highlighted. For the coefficients that are

marginally significant (p≤0.10) the figures are marked by boldface.

Italy and Latvia are the two countries where job motivation for the first migration continues

to be significant and positive even after introducing several control variables5. Residence in

Slovakia (with p=0.10) could be also hypothesized to have a considerable positive impact on

the intensity of job motivation for migration. Romania is no longer a place of significant

association between country of residence and job motivation for the first migration when one

works with the multivariate model. (The statement is valid by taking into account that the

analysis operates with a reference category for countries, UK in this case. Strictly speaking,

the finding is that residence in Romania does not have an impact on job motivation that is

significantly different from the same effect for the case of UK, the reference category.) This

means that here, in this case, the country effect on the migration motivation related to the

search for better jobs abroad disappears when the composition of the population is

considered. The more complex type of analysis in Table 6 allows for a hierarchy of the

impact of country residences on the intensity of job motivation for the first migration. The

maximum impact from this point of view is for the case of youth from Latvia, followed by

the cases of youth from Slovakia and Italy.

The youth from Italy, Spain, Germany and Sweden manifest a lower intensity of personal

community motivation for their first migration compared to the youth from the UK (Table

6). The multivariate results are consistent with those of bivariate analysis from Table 3. The

change in the findings with the more complex analysis is that the youth from Romania and

Ireland no longer manifest a significantly higher propensity for networks personal community

motivation of their first migration. Even if one changes the country reference category

(replacing UK by Slovakia, for example) the result is the same, indicating insignificant

5 The interpretations of the specific impact of residence country on different dimensions of migration

motivation by multivariate analysis are affected by the choice of the reference country that is not explicitly included into the analysis. UK is the reference country for the analysis in table 3. As mentioned, the choice results from the fact that the motivation profile of UK is highly specific (Table 3).

18

impacts of residence in Ireland and Romania on personal community motivation of

migration. This small statistical experiment underlines the idea that, in fact, the impacts of

networks abroad on the first migration for the Romanian and Irish youth are not significantly

high. What is clear is the fact that such an effect is significantly lower for the youth from

immigration countries like Germany, Sweden, Italy and Spain. It is likely that the patterns of

motivation of migration are moving in the same direction for the case of smaller countries

like Latvia, Slovakia, Romania and Ireland.

The findings from Table 3 referring to the higher impact of residence country on adopting a

lifestyle motivation are maintained for the cases of the youth from Romania, Germany and

Italy, even after controlling for several population composition variables (Table 6). Latvia

and Slovakia are no longer places of significant lower level of adoption of lifestyle

motivation for the first migration.

The significant and positive effects of country residence on education motivation for

migration (in the context of multivariate analysis) are only present for Italy and Spain (no

longer Germany and Sweden). As for the opposite situation, it is only for the case of Latvia

that the youth has a very low propensity of migrating for education reasons.

The residual or personal reasons of migrating are significantly higher only for the youth

from Germany and, partially for the youth from Sweden (see p=0.098 in Table 6). At the

opposite end, Romanian youth manifests a significantly lower propensity for this type of

highly individualised motivation.

The first migration is gendered by its motivation patterns (see first row in Table 3): men

had a higher probability to adopt job and networks motivations; women were more inclined

to adopt lifestyle motivations. The finding could be interpreted in the sense that men are more

inclined, compared to women to use strong ties as a vehicle and economic motivation for

migration. Education and residual motivations are not significantly gendered.

First migration motivation of youth is also selective by age: younger persons are guided

mainly by education and networks reasons; job reasons are specific for those closer to higher

ages, closer to 30 years old.

Other selectivity factors related to education, occupation and residence play differently for

the motivation profile of the youth:

19

Being unmarried and without children favours lifestyle migration and the opposite

situation of being married with or without children favours the migration by networks and

networks motivation.

Non-tertiary education is specific for the migrants moving on grounds of family or

friendship networks and higher education fosters cross-border movements by education

reasons.

Manual occupation per se does not engender specific types of motivations but

administrative jobs are more favourable to migration through personal networks.

Large town residence likely increases the propensity for job and education motivation

of migration. Lifestyle motivation seems to be specific for the youth from small towns.

The socio-economic profile of NUTS2 residences does not have a significant impact

on the intensity of the migration motivation6.

Status and location variables explain a rather small part of the variation in the intensity of the

five types of migration motivation (see R2 values at the bottom of Table 3). The most

predictable motivation is that related to education (18% of its variation) and the least

predictable ones are those related to lifestyle (4%) and personal reasons (5%).

… by motivation types

The previous analysis detailed the key determinants of the intensity of the first migration

motivation for the youth. The next table (no. 7) introduces the determinants of motivation

structure of migration as measured by the typology already presented in Table 5. It tries to

answer the question on ”what are the best predictors (out of the 21 already used for the

intensity of motivation) for the eight types of migration motivation. The type of

(multinomial) regression that is used in this case provides the answer to this question by

6 The three indicators referring to the NUTS2 (GDP per capita, density and life expectancy at birth) are kept

into the models from table 6 in spite of the fact that they do not have significant impacts on motivation variables. Running the five regression models without them has an impact on the significance levels of the other predictors. Regional development variables are part of a good specification model to predict migration motivation and behaviour. This is why we will keep them in all the models predicting intensity/structure of migration motivation and, also, into the models of migration experiences.

20

choosing the cumulative middle level motivation as a reference one (see Table 4 for the

identification of motivation typology)7.

The comparison between the two approaches is favoured by the fact that the unidimensional

types of motivation (mainly on education, job and networks) are expected to have a similar

causal pattern as for the corresponding dimensions of the intensity of motivation. Similar, but

not identical, because for this new analysis the approach is a comparative one: all the

coefficients for a certain type get significance only in relation to the reference category

(cumulative middle level one).

The youth in the category of mainly job motivation are negatively defined as being,

essentially, non-students from other countries than Germany and Sweden. This is consistent

with the findings for the determinants of the intensity of job motivation for migration (Table

6). The determinants for the intensity of this type of motivation are much richer (including

also age, gender and occupation) than the determinants for belonging to the job category of

motivation in the series of the eight types.

The rule is different for predicting the belonging of a migrant to the category of being

motivated mainly by education: the significant predictors are in equal number (11) in the

approach focused on intensity versus the approach interested in belonging to a certain

motivation category. In spite of this formal similarity, the differences in causal configurations

are consistent. One clear example from this point of view is on the role of education. Strictly

speaking (for a significance level p≤0.05), no local residence type (large city, small town

versus rural) predictor proved to be significant for the intensity of educational motivation on

migration (Table 6). The change of perspective on the dependent variable brings a different

result: youth from large cities and from small towns are significantly more oriented to adopt

educational reasons to migrate (Table 7). At the country level, the intensity of education

motivation is specific for the youth from Italy and Spain. The high propensity for adopting

education motivation versus other types of motivation is specific not only to Italy and Spain

but also to Sweden.

For the youth belonging to the category of educated motivated people for migration, those

who are highly motivated by educational reasons tend to be located in the NUTS2 regions

that are better developed from the educational point of view (observed level of significance is

7 There are double justification for that related to its modal value by frequency distribution and, secondly, due

to its significance of mean intensity motivation.

21

of 0.086). No similar impact at regional level is recorded when one explains the intensity of

education motivation. Higher regional GDP per capita also increases the probability of

belonging to the type of network motivation.

The bi-dimensional motivations of migration involving lifestyles – in conjunction with

networks, job and personal problems - have very different patterns of determination. Youth

from Romania, Ireland, Italy and Germany are involved in such forms of motivation: German

and Swedish youth are mainly adopting the motivations combining lifestyles and personal

problems; for the Romanian and Irish youth the pattern is to combine getting a new job with

changing lifestyle; the combination between using personal networks abroad and searching

for a new cultural environment is specific only to young Romanians. The findings provide a

good understanding of the relation between country of residence and lifestyle motivation, as

revealed initially by the analysis of data from Table 6. The key finding is that lifestyle

combines with different other motivations to explain the first migration.

The standard pattern seems to be a combination of lifestyle with the most intense motivation

in the reference country (hypothesis H4, generated by exploring the data). The most intense

motivations for German youth migration are those related to personal problems, education

and lifestyle (Table 3). Consequently, according to the previous hypothetical rule, one can

expect having to record a significant impact of having residence in Germany with a

motivation for migration that combines lifestyle with personal problems or with education.

This is exactly the case when one records a significant impact of being from Germany with

the migration motivation of “escaping personal problems and getting a new lifestyle” (Table

7). Networks and jobs are secondary motivations of youth emigration from Germany and, in

line with the previously mentioned hypothesis (identified by exploring the data), the

motivation combining lifestyle with each of these does not play a positive role in stimulating

youth first emigration. Networks & lifestyle and job & lifestyle motivations seem to be

marginal in stimulating the first emigration of youth from Germany.

The other relevant case for the topic is the Romanian youth. Here, the most intense

motivations of migration relate to job, networks and lifestyle. The empirical finding is in

accordance with H4, given the fact that for the returned migrants in Romania higher

probabilities of migrating for networks & lifestyle and job & lifestyles motivations were

recorded (Table 7).

Italy is another country with significantly high job motivation for youth migration (Table 3).

The finding revealed in Table 7 is, again, consistent with the same H4: being a returned

22

migrant to Italy is significantly and positively associated with adopting the decision for the

first migration with job & lifestyle motivations.

Other empirical regularities for predicting lifestyle motivations are summarised below:

Women are more inclined than men to adopt bi-dimensional motivations of migration

by networks & lifestyle and escaping personal problems & lifestyle.

Age counts only for networks & lifestyle in the sense that younger migrants are more

inclined to adopt lifestyle motivations in association with having networks abroad.

Being single, unmarried without children, increases the probability of migrating with

the motivation of escaping personal problems & getting a new style of life.

No significant impact of large or small cities residence on adopting lifestyle

migration, contrary to what motivation intensity analysis indicated for the case of small towns

(Table 6).

Having secondary education seems to favour the migration that is motivated by the

combination of lifestyle and trying to escape personal problems.

Being a student motivates migration abroad mainly through education and job, not

through lifestyle reasons.

Manual occupations more than the administrative ones favour lifestyle migration

associated with searching a new job.

High density at the origin NUTS2 level favours networks & lifestyle motivation of

migration. This fact could be interpreted by considering higher territorial densities as a

favouring environment for more dense networks, facilitating in their turn network migration

in association with the desire to change lifestyles.

The pattern of escaping personal problems & searching for a new lifestyle seems to be

more frequent for the youth from socially developed regions (as measured by higher levels of

life expectancy at birth). The finding could be relevant for generating a new hypothesis (H5)

supporting the idea that social, more than economic development, generates a culture of

searching new lifestyles by migration.

23

Table 7.Predicting the types of motivation for the first migration

coef. sig. coef. sig. coef. sig. coef. sig. coef. sig. coef. sig. coef. sig.

women -0.116 0.461 0.304 0.059 -0.031 0.862 -0.051 0.789 0.261 0.102 0.672 0.000 -0.460 0.002

age -0.014 0.372 -0.061 0.001 -0.011 0.535 0.007 0.673 -0.052 0.000 0.003 0.883 -0.068 0.000

single, without children* -0.033 0.876 0.056 0.806 -0.264 0.161 0.366 0.112 -0.018 0.928 0.850 0.001 -0.470 0.004

tertiarry education* -0.053 0.797 0.756 0.000 -0.269 0.234 0.183 0.382 0.247 0.149 -0.194 0.313 0.135 0.430

secondary education* 0.231 0.253 -0.087 0.780 0.129 0.670 0.377 0.167 -0.018 0.923 0.547 0.071 0.037 0.884

manual occupation* 0.106 0.636 -0.078 0.841 0.013 0.959 0.537 0.014 0.287 0.168 0.022 0.933 0.275 0.161

administrative occupation* -0.363 0.236 0.137 0.731 0.127 0.696 0.259 0.367 -0.077 0.742 -0.779 0.026 0.129 0.552

student* -0.702 0.004 1.312 0.000 -0.401 0.085 -0.813 0.001 0.070 0.678 -0.554 0.009 -0.884 0.000

large town* -0.120 0.653 0.535 0.052 -0.570 0.053 0.098 0.738 0.193 0.417 0.131 0.668 0.218 0.396

small town* 0.062 0.798 0.767 0.008 -0.031 0.905 0.162 0.588 0.324 0.206 0.181 0.579 0.297 0.249

Latvia* 0.098 0.846 -0.517 0.276 -0.213 0.685 0.277 0.404 0.106 0.766 -0.773 0.151 -0.697 0.021

Slovakia* 0.323 0.415 0.058 0.885 0.238 0.534 0.447 0.232 0.220 0.430 -0.197 0.729 -0.423 0.252

Romania* -0.297 0.595 0.463 0.384 0.736 0.188 1.017 0.042 0.825 0.030 -0.309 0.665 -0.255 0.567

Ireland* -0.388 0.269 0.215 0.799 -0.142 0.733 1.053 0.016 0.233 0.472 -0.885 0.035 -0.087 0.878

Italy* -0.235 0.559 1.234 0.000 -0.646 0.100 0.593 0.068 0.150 0.561 0.483 0.240 0.310 0.304

Spain* 0.246 0.410 0.818 0.003 0.022 0.940 0.016 0.956 -0.173 0.522 -0.110 0.774 -0.277 0.217

Germany* -1.050 0.004 -0.633 0.026 -0.321 0.345 -0.967 0.062 -1.100 0.000 0.870 0.005 -0.628 0.023

Sweden* -0.767 0.080 0.780 0.009 0.399 0.250 -0.550 0.152 -0.565 0.061 1.176 0.004 -0.071 0.889

GDP per capita NUTS2

2015 (ln)-0.046 0.881 0.382 0.086 0.446 0.084 0.236 0.355 -0.129 0.478 -0.117 0.693 0.075 0.706

Density NUTS2 2016 (ln) 0.043 0.690 -0.038 0.653 0.032 0.728 0.051 0.672 0.151 0.061 0.115 0.196 0.114 0.181

life expectancy at birth

NUTS2, 20150.175 0.553 0.567 0.574 0.484 0.397 -0.959 0.053 -0.240 0.504 1.209 0.016 -0.240 0.716

_cons -1.044 0.629 -5.765 0.203 -4.517 0.107 0.857 0.737 1.062 0.563 -8.011 0.004 2.008 0.515

Pseudo R2 =0.088

Cumulative

upper middle

motivation

Types of first migration motivationPredictors

N=4691 Std. Err. adjusted for 594 clusters in NUTS3

Job Networks and

lifestyle

Lifestyle and

job

Education Escaping

pers.problems

and getting

new lifestyles

Networks

Return migration8

Reasons to return

Reasons to return have a different structure compared to reasons to migrate. Migrants are less

likely to think of return in terms of spheres of life as in the case of temporary emigration. The

set of 17 questions which were designed to capture their reasons for returning were

formulated in the YMOBILITY survey in terms which were as similar as possible to those

used to capture their first migration reasons. However, the natural grouping that resulted from

the data analysis is different. The return reasoning of the migrants is more likely to centre on

solving problems, considering constraints and achieving the initial plans behind their decision

to emigrate. Emigration was basically driven by relative frustrations and opportunities related

to jobs, education and way or style of life. Return is informed more by keeping or forming a

family, reacting to constraints (of health or work contracts), doing business at home or

building a house, or generally accomplishing the initial aims of the emigration.

8 Dumitru Sandu, Paula Tufis

24

The 17 specific motivations of return are reduced to four dimensions of family, personal

health, achievement of migration aims and business & and having one’s own house (Table 8).

Table 8.Reducing the diversity of return motivations to four latent dimensions

FAMILY HEALTHACCOMPLISHED

PLANBUSINESS

for children 0.677 0.24 0.146 0.289

to take care of the family 0.641 0.271 0.153 0.264

homesickness 0.618 0.245 0.163 0.09

to form family 0.59 0.263 0.197 0.229

welfare 0.577 0.275 0.243 0.249

chiper cost of living 0.504 0.385 0.218 0.287

job prospects 0.402 0.297 0.227 0.361

health problems 0.443 0.611 0.133 0.195

company transfer 0.299 0.558 0.237 0.439

work permit expired 0.241 0.508 0.313 0.243

personal problems 0.485 0.494 0.133 0.078

difficult socio-cultural environment abroad 0.471 0.493 0.188 0.258

as planned 0.085 0.091 0.818 0.088

to complete studies 0.145 0.241 0.615 0.11

migration aims achieved 0.378 0.097 0.556 0.182

business 0.383 0.358 0.203 0.527

to have own house 0.492 0.25 0.213 0.524

N=4926, Weighted data.

Reasons to return homeLatent dimensions of return motivation

Factor analysis: maximum likelihood, Varimax, KMO=0.958, Chi square for goodness of fit=948, p=0.000

Each of the four dimensions covers several interrelated reasons. Family motivation includes

taking care of children and other family members, forming one’s own new family, but also

homesickness, and targeting a place with lower costs of living. The health dimensions also

include solving personal problems abroad and at home. Business motivation covers the desire

to build or to get one’s own house.

The four dimensions of the return motivation combine and generate a classification of eight

categories (or types) of return motivation (Table 9). Four of them are one-dimensional,

focused on a single dimension of motivation. There is only a single category of dual

motivation grouping youth that returned home because of family and health reasons. There

are also two types of multidimensional motivations of return, one of mid-level intensity and

another one of upper-mid-level intensity.

25

Table 9.Types of return motivations by the intensity of the specific components

The country profiles by the distributions on motivation types are presented in Table 10. If one

considers only the significant associations, the similarity groups are formed by Germany-

Italy, Ireland-UK and Latvia-Romania- Slovakia (Table 10). Youth from Germany and Italy

have the highest proportions of returnees motivated in their coming back by the simple fact of

reaching their planned target, before leaving the origin countries. In fact, Germany is also

similar to Sweden, both having the same high proportion of youth coming back home

motivated by reaching the initial targets. Ireland and UK are having by far the highest

proportions of returnees of cumulative motivations of mid-intensity. Latvia and Romania are

the two countries with the highest proportions of returnees driven by family reasons to come

back home. But Slovakia is also very close to them in terms of proportion of returnees driven

by the same reasons.

Coef. Coef. Coef. Coef.

human capital 0,048 0,077 ** 0,138 *** 0,140 ***

job 0,117 * 0,048 0,080 *** 0,021

welfare 0,115 ** -0,053 * -0,046 ** -0,027

ammenities -0,107 ** 0,054 * 0,019 0,000

age -0,044 *** -0,042 *** -0,038 *** -0,047 ***

man* 0,102 * 0,164 *** 0,116 *** 0,142 ***

tertiarry educ.* -0,098 * -0,022 0,086 *** 0,121 ***

large town resid.* 0,054 0,061 0,100 *** 0,179 ***

circular* 0,733 *** 0,522 *** 0,911 *** 0,603 ***

one time return* 0,457 *** 0,350 *** 0,511 *** 0,285 ***

involuntary stayer* 0,815 *** 0,760 *** 0,860 *** 0,763 ***

health* -0,143 ** -0,028 -0,014 0,033

job* -0,057 -0,197 *** -0,132 *** -0,071 **

education* -0,108 0,175 * 0,131 *** 0,126 ***

family* 0,023 -0,108 ** -0,090 *** -0,110 ***

standard of living* -0,108 -0,103 * -0,054 -0,054 *

_cons 3,954 3,507 3,440 3,543

R2 0,127 0,133 0,182 0,1356

N 2078 4003 9976 13622

* dummy variables

inde

x of t

he

reas

ons

relat

ed to

..

dem

ogra

-

phics

migration

experience

(reference

voluntary

satis

fied

with

Predictors y=intention to migrate in the next 5 years (5 points scale) for

youth from...

Romania

Latvia and

Slovakia Italy and Spain DE UK IE SE

Note: The typology of the return motivation resulted from a k-means cluster analysis with predetermined cluster centres. The values in the table are means on intensity of motivation for the specific cluster and dimension. Example: the average intensity of the home motivation for the returned migrants in the category of mainly home motivated persons is of 74. The four dimensions of the intensity in motivation for return are the result of a factor score on 17 indicators of return motivations, transformed to have a variation approximately between 0 and 100.

26

Table 10.Typology of the return motivations by residence countries (%)

Germany Italy Spain Sweden Ireland UK Latvia Romania Slovakia Total

family 4 7 6 4 2 3 20 20 15 7

health 2 4 2 2 2 2 3 2 5 2

accomplished plan 38 30 22 27 10 12 9 18 26 21

business 2 9 3 3 2 2 4 11 8 4

home and health 5 3 3 5 5 1 18 9 8 5

cumulative upper level

motivation

5 12 5 4 3 6 2 6 4 5

cumulative middle level

motivation

45 36 59 55 76 73 44 35 35 55

100 100 100 100 100 100 100 100 100 100

Types of return migration motivation

one-

dimensional

multi-

dimensional

Total

Note: Figures in the table are percentages out of the total returned migrants in the country

in the reference category on the row. Highlighted cell represent significant associations

between column and row values (adjusted standardised residuals, not shown in the table).

Example: 76% out of the total youth that returned home in Ireland are in the category of

cumulative middle level motivation for their return migration. This is a specific, significant

motivation for the Irish youth returned migrants.

The similarity pictures among return migration motivation profile of the youth is rather

unstable, function of the input data and clustering techniques (see figures 3 and 4). There are

only two pairs of similarity that are invariant – Latvia-Romania in the series of New Member

States (NMS) of EU and Spain- Sweden in the grouping of EU15.

27

A.Dendrogram by four factor scores of return

migration motivation as described in Table 8

B. Dendrogram by 17 return motivation

indicators recoded as dummy variables

C. Dendrogram of country profiles as presented in

Table 10

Note: All three dendrograms are generated by

cluster analysis: Pearson’s correlations among

z-scores were grouped by the furthest

neighbour (complete linkage) hierarchical

clustering algorithm. The higher the similarity

between the migration profiles of the two

countries, the closer to zero dissimilarity scale

the line uniting them. Example: Ireland and UK

are the most similar countries by their profiles

for the first migration of the youth (approach

from figure A).

Only Spain and Sweden appear in the same

clusters, irrespective of the input data.

Figure 3.Clusters of country profiles function of reasons for returning home

28

level of

similarity

among

profiles

0.85 Spain UK Ireland

0.8 Germany Sweden

0.75

0.7

0.65

0.6

Similarity of the first degree

0.55

Similarity of the second degree

0.5

0.45

0.4 Latvia Romania Slovakia Italy

0.35

Figure 4.Networks of similarity among country profiles by proportions of youth into the

seven types of return motivations

The UK and Ireland is also a rather stable pair of motivation similarity. It appears as such in

three out of the four figures (Figure 3 A and C and Figure 4). This rather high instability

coming from the input data and clustering method support the idea that, in fact, the nine

surveyed countries in YMOBILITY are, in fact, structured not so much as clusters or

groupings but more as a network of similarity. Latvia-Romania and Spain-Sweden are nuclei

of similarity in these networks. The understanding of the complexity of relations is provided

by considering together table 10 and figure 4. Spain (meaning by this the youth from Spain)

is highly similar with the youth from UK under the aspect of the cumulative middle level

motivation for return. But Spain is also similar with Sweden in one considers the rather high

shares of youth coming back because they consider accomplished their emigration plans.

Romanian youth is similar to the Latvian youth especially by family, and home & health

motivation for return. But at a lower degree, the Romanian youth is similar to the youth from

Slovakia, by the same criteria.

Predicting reasons to return

All the discussed diagrams are built on bivariate relations of similarity between motivation

profiles of the youth from the nine countries. Introducing a large set of control variables

(Table 11) makes clearer what are the net effects of countries of residence on assuming a

certain returning motivation.

29

The new more precise frame of analysis reconfirms the existence of two similarity nuclei

Romania-Latvia and Spain-Sweden. Romanian and Latvian youth are significantly more

oriented to return home on the ground of family reasons or family and health reasons.

Slovakian youth are also motivated by the two categories of reasons as Latvian and

Romanian youth. But they are also committed to health, business and accomplished plans,

like the Italian youth. It is in this context that one can understand to motivation profile of the

Slovakian youth as having two facets, one towards the youth from Romania and another one

towards the youth from Italy. Geography and history validates the interpretation.

The case of the Irish Youth is also reconfirmed by the multivariate analysis from the table 11.

Its motivation profile differs from the profile of the British youth only for one category of

reasons: Irish youth is less committed to the return motivation of accomplished plans,

compared to the British youth.

Table 11.Predicting the types of return migration motivations

coeff. sig. coeff. sig. coeff. sig. coeff. sig. coeff. sig. coeff. sig.

women* -0.034 0.828 -0.788 0.009 0.404 0.033 0.212 0.087 -0.499 0.011 -0.710 0.000

age 0.101 0.000 -0.016 0.519 0.080 0.000 0.040 0.000 0.038 0.089 0.024 0.276

single, without children* 0.300 0.143 -0.051 0.886 -0.225 0.245 1.342 0.000 0.680 0.010 0.081 0.678

tertiarry education* 0.398 0.043 0.695 0.023 -0.337 0.202 0.706 0.000 0.748 0.009 0.530 0.023

secondary education* -0.043 0.851 -0.128 0.804 -0.495 0.051 -0.072 0.670 0.277 0.383 -0.754 0.024

manual occupation* 0.595 0.018 1.443 0.000 0.083 0.728 0.342 0.090 0.536 0.093 0.434 0.088

administrative occupation* 0.867 0.004 1.531 0.006 0.533 0.082 0.687 0.013 1.009 0.007 0.663 0.050

student* 0.520 0.025 0.117 0.778 0.101 0.716 1.240 0.000 0.149 0.613 -0.137 0.581

large town* 0.169 0.503 0.641 0.313 -0.197 0.537 0.039 0.826 -0.238 0.474 0.817 0.092

small town* -0.185 0.483 -0.002 0.998 -0.256 0.351 -0.008 0.960 -0.332 0.335 0.514 0.276

Latvia* 2.202 0.000 -0.095 0.861 2.085 0.000 -0.239 0.560 0.332 0.548 -0.467 0.382

Slovakia* 1.878 0.000 1.175 0.054 1.598 0.001 1.029 0.001 0.933 0.041 0.786 0.192

Romania* 2.077 0.010 -0.150 0.827 1.303 0.049 0.822 0.130 0.972 0.061 1.059 0.188

Ireland* -0.628 0.255 -0.293 0.712 0.683 0.159 -1.035 0.006 -0.426 0.554 -1.160 0.191

Italy* 1.539 0.000 1.027 0.029 1.148 0.010 1.492 0.000 1.862 0.000 1.230 0.000

Spain* 0.763 0.027 -0.040 0.924 0.575 0.162 0.503 0.016 0.277 0.546 0.134 0.599

Germany* 1.040 0.002 0.260 0.554 1.962 0.000 1.573 0.000 0.283 0.527 0.345 0.236

Sweden* 0.822 0.022 -0.412 0.532 1.585 0.000 0.937 0.010 1.091 0.027 -0.478 0.217

GDP per capita NUTS2 2015

(ln) -0.504 0.106 -0.159 0.624 -0.801 0.012 -0.276 0.363 -0.842 0.008 0.138 0.724

Density NUTS2 2015 (ln) 0.307 0.542 -1.719 0.087 -0.111 0.887 0.735 0.063 -0.527 0.603 0.522 0.647

life expectancy at birth,

NUTS2 2015 0.062 0.534 -0.231 0.064 -0.058 0.553 -0.157 0.012 0.085 0.584 -0.154 0.081

constante -5.946 0.022 5.775 0.236 -1.164 0.746 -5.879 0.009 0.828 0.861 -5.744 0.257

Pseudo R2=0.15

Predictors

Family Health Family and

health

Std. Err. adjusted for 625 clusters in NUTS3 N=5469

Types of return migration motivation

Accomplishe

d plans

Business Cummulativ

e upper

middle

motivation

Source: YMOBILITY survey, 2015. Multinomial regression with type of

return motivation as dependent variable (reference category – cumulative

middle level motivation). * dummy variable. UK – reference category for

country of residence.

30

The similarity between return motives for the youth from Germany and Sweden is another

empirical evidence from the multinomial regression. There is a high propensity in these two

cases for returning home by family, family & health, and accomplished plans reasons.

A rather surprising finding is that returning home tends to be more frequent in poorer than in

more economically developed regions. This is valid especially for those adopting mainly,

health or entrepreneurial reasons. Further exploration to clarify the trends is necessary. One

possible hypothesis could be that human and material capitals accumulated abroad

could be better valued in poor than in developed regions.

Looking for information, channels and destinations9

How was the first migration “organised”: country and status differentials

Even if the diversity of channels to go abroad is very large, it can be reduced to three main

patterns related to institutions, friends&relatives, and migrants as individuals. The personal

search for opportunities seems to be the dominant pattern of organizing the migration: 23%

are doing the search when already at destination and 13% from at home, in the origin country

(Table 12). The second royal rout of moving abroad is the networks involving friends and

relatives (23%). The third key rout for the surveyed youth is related to student mobility

programs or to the agencies recruiting students (21%).

A regrouping of the eight channels gives five main routes. It is very likely that there is a high

specificity of countries by dominant channels of migration function of the resources available

to migrants. Temporary emigration from the CEE countries, for example, is expected to be

highly dependent on family and friendship networks due to the reduce number of agencies of

labour recruiting or the international exchange programs for student mobility.

9 Dumitru Sandu, Paula Tufis

31

Table 12.Channels for the first migration

Categories of

channels

”How was your movement 'organised' when you

first left your home country to live abroad?” %

Company policy 1.9Private job recruitment agency in my country 11.6Private job recruitment agency in the destination country 6.0Services of student recruitment agencies 4.4Student Mobility programs / a scholarship

16.9personal from

origin

Direct contact with employer while still in my home country12.6

personal at

destination

I went looking for an opportunity

23.2friends and

relatives

Friends/relatives/ acquaintances already working abroad23.4

Total % 100N 4156

agencies at

orig or dest

student

mobility and

recruitment

Data source: YMOBILITY survey 2015

Two out of the three countries in this category, included into the YMOBILITY survey,

proved to be with a situation that is consistent with the above-mentioned expectation (Table

13). Slovakian youth has a different pattern of using agencies at origin or destination for

reaching the opportunities that they look for as migrants.

Table 13.Distribution of the first migrants by channels and countries of origin

agencies at

orig or dest

student

mobility and

recruitment

personal

from origin

personal at

destination

friends and

relatives % N

Germany 20 23 10 33 14 100 538

Ireland 11 18 10 30 31 100 415

Italy 19 28 18 15 20 100 482

Latvia 15 10 17 10 48 100 334

Romania 15 13 11 9 52 100 228

Slovakia 33 12 19 13 24 100 338

Spain 19 30 9 22 20 100 570

Sweden 20 26 14 28 12 100 380

UK 21 21 11 30 17 100 873

Total 20 21 13 23 23 100 4158

TotalMigration channels

Origin

country

Data source: YMOBILITY survey 2015. Figures that are marked by shadow indicate

significant association between column and row values (results of adjusted standardised

residuals that are not presented to not complicating the reading of the data).

Youth from emigration countries (UK, Germany and Sweden) relay mostly on personal

search at destination. The pattern is consistent with the situation of emigration networks and

migration cultures that are highly structured for these countries. For Spain and Italy as

32

emigration-immigration countries the dominant channels is that of student mobility and

recruitment. Ireland diverges from this model relaying on personal community networks and

searching at the destination for desired opportunities.

Summarizing, one could notice that migration channels follow, basically the patterns

that are specific for emmigration, immigration or emigration-immigration country.

The strong ties of associations among certain migration channels and specific countries are

visible only from table 14. The net effect of the country of residence on adopting a certain

migration channel is identifiable here by introducing a large set of control variables related to

the status of the person before the first migration by age occupation, urban-rural residence,

marital status, network capital on relatives-friendship connections and regional development

at NUTS2 level for the current residence10

. Education is recorded only to the survey moment

and we considered its current values as proxies for the pre-migration moment.

The net effects of residence countries on the adoption of a certain channel for migration

(Table 14) are only partially overlapping with the patterns indicated by the bivariate analysis

(Table 13). Determinants of migration channels will be considered in the next paragraphs in

relation with other spatial determinants related to the NUTS2 regional development and to

the urban-rural residence.

10

The survey questionnaire recorded also the region of residence (NUTS2) before the first migration. The problem is that what we need for analysis are the values of economic and social indicators at regional level land not only the name of the region. The indicators values are different for different years and it is hard to work with values of the same indicator for different years, function of emigration moment. Consequently, we used the values for NUTS2 GDP per capita as percentage of EU mean only for 2015. GDP and life expectancy do not vary so much for short periods of time. This is why we accepted to use them as proxy variables for the pre-migration moments.

33

Table 14.Predicting the adoption of a certain type of channel for temporary emigration

coeff. p coeff. p coeff. p coeff. p

primary or secondary education* 0.613 0.012 -0.350 0.146 0.568 0.017 0.650 0.008

post-secondary education before tertiarry* -0.015 0.940 -0.372 0.035 0.303 0.075 0.781 0.000

male* 0.297 0.096 -0.316 0.056 -0.395 0.007 -0.559 0.000

age -0.051 0.000 -0.027 0.034 -0.056 0.000 -0.072 0.000

with friends or relatives abroad* -0.267 0.140 -0.197 0.191 -0.318 0.035 1.063 0.000

single* -0.086 0.660 0.472 0.027 0.552 0.003 0.186 0.345

manual work* -0.187 0.426 -0.034 0.891 -0.181 0.416 -0.280 0.217

clerical job* -0.293 0.201 0.049 0.871 -0.019 0.935 -0.437 0.064

student* -0.060 0.769 1.896 0.000 0.209 0.298 0.084 0.693

living in a large city* 0.574 0.022 0.262 0.357 0.250 0.249 -0.062 0.802

living in a small city* 0.355 0.186 0.420 0.118 0.133 0.565 0.047 0.851

Germany* -0.406 0.235 0.216 0.541 0.976 0.012 -0.196 0.641

Ireland* -0.643 0.076 0.116 0.773 1.075 0.011 0.570 0.204

Italy* -0.842 0.007 0.597 0.024 -0.063 0.843 -0.221 0.562

Latvia* -0.784 0.021 0.118 0.716 -0.078 0.833 0.856 0.060

Romania* 0.064 0.892 1.213 0.007 0.549 0.250 1.374 0.000

Spain* -0.016 0.955 1.161 0.000 1.222 0.000 0.573 0.098

Sweden* -0.870 0.016 0.430 0.272 0.646 0.099 -0.429 0.331

United Kingdom* -0.633 0.041 0.473 0.104 0.919 0.008 0.111 0.778

GDP per capita NUTS2 2015 (ln) 0.639 0.001 0.323 0.128 0.395 0.022 0.159 0.471

life expectancy at birth NUTS2 2015 (ln) -0.448 0.279 1.224 0.023 0.168 0.743 0.335 0.457

constante 0.805 0.687 -7.378 0.004 -1.776 0.447 -1.093 0.595

Self-reported channels for the first migration

Predictors

Data source: YMOBILITY survey. Multinomia regression with standard errors adjusted for 597 clusters in NUTS3 current

residence regions.N=4767. Pseudo R2=0.129. Weighted data. Reference category for the dependent variable - personal search at

destination when being still at origin. Reference categories for independent variables: village for local residence, Slovakia for

residence country, married for civil status.

curr

ent

loca

tio

n

gender

and

educ.

per

son

al s

tatu

s b

efo

re

the

firs

t m

igra

tio

nagencies at origin or

destination

student mobility

or recruitment

personal search at

destination

friens and

relatives

Friendship and network channels. Bivariate analysis indicated Romania, Latvia and Ireland

as having a significantly higher propensity for the youth to emigrate on the way of relatives

and friendships networks. The new multivariate approach confirms the expectation only

for the case of the Romanian youth. With a higher probability of error (p between 0.05 and

0.10), the youth from Latvia and Spain adopt also the relatives & friends route to going

abroad in a significant degree (Slovakia does not appear into the table as reference category

for the discussed independent variable). The impact of the country residence on adopting this

pattern of emigration is maximum for the Romanian youth (compare values of the regression

coefficients for all the countries and the reference category of channels). The route of

personal communities (=friends and relatives) is complementary to the institutional route of

agencies. This last one route is insignificant for Romanian and Spain and of a negative

significant pattern for the youth from Latvia.

It is not clear why personal communities are much more important for the youth migration

from Spain. But it is easier to interpret the relation for the case of Latvia and Romania.

Here it seems to be the result of the fact that the migration industry is less developed

than in the other surveyed EU countries. It is in this context that personal communities are

the functional substitute for the institutional development.

34

Regional development and urban-rural residence are not significant predictors for adopting

personal community channels.

The institutional channels of using agencies at origin or at destinations are mostly used by

the youth from Slovakia (Table 13). Multivariate analysis from the table 14 indicates that

none of the other eight survey countries are associated positively and significantly with the

practice of adopting this type of channel for emigration. A partial technical explanation is that

Slovakia is a reference category in this model of analysis and its choice for this role brings

automatically Italy and Latvia in the position of having a much lower frequency of use for the

specified channel. It seems that not the choice of the reference category is the explanation of

the situation as far as running the model with Romania a reference category gives the same

results as having Slovakia in the position of a reference category.

This type of channels is the only one that is dependent on regional development of the

residential area of the migrant: the higher the economic development of the residence

NUTS 2, the higher the propensity of adopting agencies as routes to reaching the

destination countries. It is not the social (measured by life expectancy at birth) but the

economic development (identified by GDP per capita as percentage from the EU mean) that

counts. The probable explanation could go into two directions. One the one hand, one could

hypothesizes that economically developed regions at origin have a better environment for the

settlement of labour recruiting agencies. Secondly, one could expect having a better-

structured culture of circular migration in these areas with people used to get

predictable opportunities of migration through institutionalised channels in the series of

labour agencies.

It is also specific for the youth from large cities to migrate through the institutional route of

the labour agencies at home or at destination. The sociological interpretation could be the

same as for the case of the role played by economic regional development as a favourable

factor for more and better agencies for labour recruiting in home societies. There is no other

type of migration channel that is favoured by residence into large cities.

Personal search of migration opportunities at destination is specific for the youth from

immigration countries (Germany, UK, and Sweden for p=0.10) and, also, for Spain and

Ireland (as emigration-immigration countries). All these are countries with well-structured

diasporas and long time migration experience abroad. These are the factors facilitating a

friendlier environment for the new migrants to finding directly abroad jobs and housing

35

facilities. As expected, Latvian and Romanian youth are not associated with this type of

channel due to the rather young age of their diasporas in the host countries.

Mobility programs and recruitment agencies for students. This type of channel is specific

for emigration and immigration countries (Spain and Italy) and, also, for Romania. Romanian

residence has the highest impact on adopting the student institutional channel. Why these

countries are more favourable to the youth migration through the programs of student

exchanges is not clear.

Status predictors of channels adoption at the pan-European level. There is a clear gender

selectivity in the adoption of migration channels. The option for labour agencies is specific

for men (even if only for p=0.10). All the other channels are preferred by young women. The

highest impact of gender is on the adoption of friends and relatives as key route to going

abroad. Education selectivity of migration routes is also of high visibility: less educated youth

prefer agencies at home or at destination, personal search at destination and friends or

relatives as connection points on this route. There is also a significant segment of youth that

practices emigration by personal communities in the category of those that are post-secondary

but non-tertiary educated.

The age selectivity in this area makes the differentiation between adoption of personal search

of opportunities from at home (reference category for the dependent variable) and all the

other four types of channels. Those in the first (reference) category are older than the youths

adopting the other means of emigration.

The single, unmarried youth prefer the migration as students or by searching directly

opportunities at destination.

Having friends and relatives abroad makes the difference in the adoption of two categories of

channels. Those that are rich in personal connections abroad are, normally, using them to a

high degree and, complementary, are giving up the use of direct search at destination.

Occupation does not seem to be highly significant for channels selection. It is only for those

of non-clerical occupation to have a higher probability to using friends and relatives as

resources for migration.

Gender as predictor of channels adoption country by country. The causal pattern

revealed above is valid in a rather abstract way, for a kind of ”middle youth” in the nine

European countries surveyed by YMOBILITY. Repeating the analysis with the same

36

predictors country by country the image is different. Some of the European causal relations

are also identified to the majority of countries but some other ones not (table 15).

Gender, for example, continues to be an important predictor of using the social networks

involving friends and relatives to migrate. Women have the highest probability to adopt this

channel of migration in Ireland, Romania, Slovakia, Spain and the United Kingdom. The

gender selectivity of this type of migration channel is maximum for the case of Romanian

youth.

Education as predictor of channels adoption country by country. Non-tertiary education

is also a favourable factor for migrating with the help of friends and relatives. Low education

has a maximum intensity impact of this type in the Latin countries (Italy, Spain and

Romania). Post-secondary education before the tertiary level is favourable to the same type of

migration channel especially for UK, Sweden, Spain and Slovakia.

Having friends and relatives abroad brings higher probability of using them as a means for

migration at the level of the nine surveyed countries. Youth from Romania, Latvia and Spain

are particularly oriented in this direction (Table 14). Case studies of the same relation in each

country bring a surprising result. The relation is the same in eight out of the nine countries

(higher stocks of friends and relatives abroad brings higher propensity to migrate through this

channel). The exception is the youth from Romania. One does not record here a significant

and positive relation between having friends and relatives abroad and using them as such for

going abroad. It is not clear why this exception. A possible explanation could be related to

the fact that the stock of friends and relatives abroad is so high here (see Table 15) that

within country variation from this point of view is rather low and brings an

insignificant correlation.

Regional development as predictor of channels adoption country by country. Higher

values of GDP per capita at regional level favour, at the pan-European level (Table 14),

higher probability of adopting the use of labour recruiting agencies as a route to migration

abroad. The relation is valid, when analysing county by country (Table 15) only for the case

of Ireland and (partially for p=0.10) for the UK. The second situation of a positive impact of

regional development – for the whole sample for the nine countries - is in relation with

adopting personal search of opportunities at destination as a channel of migration. The

relation is no more valid when analysing the same impact country by country.

37

Table 15.Predicting the adoption of a certain type of channel for temporary emigration by

countries

coeff coeff coeff coeff coeff coeff coeff coeff coeff

primary or secondary education* -0.118 0.282 16.551 *** 1.046 1.210 0.998 * 0.766 1.809 * 0.804

post-secondary education before

tertiarry* -0.236 0.010 0.436 0.925 -1.977 1.723 * 0.112 0.218 -0.648

male* 0.202 1.497 0.484 -0.198 0.060 -0.313 -0.126 1.214 0.072

age -0.060 * -0.002 -0.088 * 0.016 -0.004 -0.076 * -0.099 *** -0.027 -0.062 *

with friends or relatives abroad* 0.192 -2.039 *** -0.327 0.439 -2.697 *** -0.774 *** -0.431 0.180 -0.023

single* 1.198 -1.334 -0.283 -0.293 1.228 -0.403 -0.707 0.601 -0.477

manual work* 0.523 0.084 -0.445 1.186 -1.107 -1.068 -0.765 -0.553 -0.461

clerical job* -1.591 1.732 *** 0.576 0.438 0.646 -0.582 * -0.518 -0.983 -0.066

student* 0.064 1.427 *** 0.650 1.507 ** -2.109 -1.178 0.279 -0.024 -0.795

living in a large city* 0.649 0.149 2.193 ** -1.143 0.428 0.277 0.418 1.226 0.941

living in a small city* 0.061 -0.190 1.403 * 0.425 -1.858 0.616 0.270 1.489 0.524

GDP per capita NUTS2 2015 (ln) 1.525 5.929 *** -0.940 0.790 0.004 -0.236 -0.396 -0.540 0.572

life expectancy at birth NUTS2

2015 (ln) 3.715 -1.081 19.932 3.093 -0.318 33.567 15.479 0.186 0.670

primary or secondary education* -0.620 -0.553 15.951 *** -2.596 * -0.975 -0.857 -0.101 -0.398 -0.263


tertiarry* 0.528 -0.656 -0.760 -2.017 * -17.326 *** -1.053 -0.443 -0.121 -0.368

male* 0.079 0.430 -0.070 0.181 -2.751 *** -0.291 -0.667 0.310 -0.885 **

age -0.005 0.026 -0.077 * -0.139 -0.103 -0.137 ** 0.028 -0.047 * -0.035

with friends or relatives abroad* -0.266 -0.040 -0.570 -1.808 *** -1.950 * -0.123 -0.350 0.376 -0.020

single* 1.346 ** 1.097 -0.075 0.268 1.460 -0.686 0.200 0.287 0.752

manual work* 0.493 -0.484 -0.055 -0.775 0.205 -0.391 -0.368 -0.218 0.089

clerical job* -1.092 2.082 *** -0.226 0.478 0.650 -2.077 -0.392 0.321 -0.136

student* 1.349 2.714 *** 2.538 *** 0.613 0.391 1.113 2.675 *** 1.586 ** 1.880 ***

living in a large city* 0.440 0.306 0.234 16.003 *** -1.059 0.407 -0.751 -0.626 1.386 *

living in a small city* 0.668 0.299 0.754 15.502 *** -2.217 1.333 -0.559 0.042 0.960

GDP per capita NUTS2 2015 (ln) 2.429 ** 3.183 * 1.114 -10.617 -1.192 0.454 -0.180 -0.505 0.095


2015 (ln) -27.354 1.497 *** -10.848 163.687 * 16.436 -16.342 15.338 12.853 -5.046

primary or secondary education* -0.204 0.158 16.708 *** -1.654 -0.098 1.175 * 0.928 0.524 0.745


tertiarry* 0.873 0.213 0.201 -0.607 -2.993 1.173 0.393 0.408 -0.143

male* -0.235 -0.181 -0.171 -0.171 0.687 -0.607 -0.615 0.456 -1.012 ***

age -0.007 -0.024 -0.055 -0.117 -0.111 -0.101 * -0.086 *** -0.063 ** -0.077 **

with friends or relatives abroad* 0.165 -0.545 -0.725 0.143 -0.432 -0.342 -0.363 0.089 -0.756 *

single* 1.328 ** -0.098 0.775 0.330 0.632 -0.379 -0.433 1.126 ** 0.717 *

manual work* 0.031 0.686 0.675 -0.329 -0.254 -2.033 ** -1.682 ** -0.550 0.118

clerical job* -0.740 1.522 *** -0.073 -0.964 1.436 -0.836 **P36:P410.164 -0.275 0.295

student* 0.217 1.395 *** 0.701 0.587 -2.153 * -1.087 -0.028 -0.076 0.269

living in a large city* 0.451 -0.794 -0.749 1.110 -0.579 0.409 0.180 0.961 0.586

living in a small city* 0.447 -0.613 -1.227 * 0.010 -2.108 0.496 0.284 0.674 0.324

GDP per capita NUTS2 2015 (ln) 1.233 2.336 0.273 -0.603 -2.482 2.673 0.581 -1.390 0.271


2015 (ln) 2.772 0.209 42.316 27.758 37.321 -84.592 -8.775 38.156 -1.313

primary or secondary education* 0.180 -0.584 16.354 *** 0.161 1.587 * 0.392 1.276 * 0.151 1.192


tertiarry* 0.685 0.091 0.410 0.526 -1.135 1.654 * 1.449 *** 1.027 * 0.941 *

male* -0.578 -0.533 * -0.129 -0.220 -1.871 *** -0.806 -0.857 ** 0.213 -0.890 *

age -0.055 -0.072 *** -0.059 0.019 0.000 -0.060 -0.107 *** -0.100 *** -0.103 ***

with friends or relatives abroad* 1.030 * 1.746 *** 0.977 * 1.920 *** 0.280 0.330 ** 1.146 * 1.279 * 0.845 *

single* 1.370 0.094 0.689 -0.238 0.832 -0.207 -0.995 0.568 0.021

manual work* 0.777 0.159 0.001 0.878 * -1.317 * -1.458 ** -2.149 *** -0.922 -0.218

clerical job* -0.851 0.214 0.116 -1.277 *** 0.540 -0.533 -0.311 -0.029 -0.274

student* 0.252 1.023 ** -0.193 0.640 -2.137 * -0.786 0.085 0.052 0.474

living in a large city* 0.227 -1.350 0.024 -0.011 -1.482 0.139 -1.274 0.879 0.837

living in a small city* 1.070 -1.179 ** -0.084 0.549 -1.741 0.483 -1.858 * 0.323 0.686

GDP per capita NUTS2 2015 (ln) 1.909 * 1.683 -0.791 0.563 -0.839 -0.342 -0.430 -1.558 -0.047


2015 (ln) 0.143 0.578 8.548 4.748 -2.248 37.123 15.548 26.667 -5.560

Pseudo R2 0.088 0.201 0.183 0.228 0.341 0.125 0.195 0.131 0.159

N 650 516 564 318 251 351 827 374 916

stu

den

t m

ob

ility

or

recr

uit

men

tp

erso

nal

sea

rch

at

des

tin

atio

nFr

ien

ds

and

rel

ativ

es

Spain Sweden UK

agen

cies

at

ori

gin

or

des

tin

atio

n

chann

els

Predictors Germany Ireland Italy Latvia Romania Slovakia

Data source: YMOBILITY survey. Multinomial regression with standard errors adjusted for 597 clusters in

NUTS3 current residence. Weighted data. Reference category for the dependent variable - personal search at

destination when being still at origin. Reference categories for independent variables: village for local residence,

married for civil status. Significance level * 0.05, ** 0.01, *** 0.001. Bold figures for p=0.10.

38

Interactions between reasons and means for the first migration

The motivation for the first migration and the means that are used to accomplish it is rather

well rooted into the status characteristics (age, gender, education, occupation, civil status) and

spatial context that is given by rural-urban residence, regional development and country of

residence (Table 7 and Table 16). It seems that channels variation is better explained than the

variation of the reasons for migration (pseudo R2 is 0.13 in the first case, compared to

compared to 0.09 in the second case). Even if in theory the means are subsequent to ends or

reasons, in fact the two are no more than facets of the agency in migration. They could be

considered in interaction. It might be useful to better understand how reasons and means for

migration are associated as a ground to generate better interpretations of the statistical

findings. It is what we are doing here shortly.

In a table (that is not presented here to avoid the overloading the text with too many figures)

crossing the eight types of motivations for the first migration and the five types of means to

accomplish the mobility project one can read what are the significant means-ends

associations (the whole analysis is based on adjusted standardised residuals):

The use of agencies at home or at destination for finding opportunities is specific for

those that have a high motivation (a cumulative upper middle motivation for migration).

Education motivation is, normally, strongly associated with access to exchange

programs of Erasmus type or to recruitment services for students.

Those that are motivated to migrate by job and lifestyle are identifying opportunities

mainly by personal search when still at home.

Means and determinants to reaching destinations by migration

Understanding migration is frequently reduced to explorations to answering the

questions”why do they leave”, ”how many”, and ”by what reasons and means”. These are, for

sure, the key ones. What is less explored is the family of questions related to the destination

choice: ”why people from the same place migrate to different destinations” and ”what are the

ends and means” to going from the same place to different destination. One of the reasons

that such questions are less explored comes from the simple fact that it is harder to getting

39

data on streams connecting specific origin and destinations. The problem is valid also here

for our YMOBILITY data11

.

A simple inventory of the streams of more than 50 persons, identified by YMOBILITY

survey, could provide some information to start the exploration of the above-mentioned topic

(Table 16).

The streams with the highest concentration in motivations are for the Romanians going to

Italy and for the Latvians going to the UK: 70% of the Romanians that left for Italy for the

first time decided to go there in relations with having friends or relatives to the Italian

destination. Over 50% of the Latvians going to the UK did so by the same reason of having at

hand friends and relatives in the destination country.

Table 16.Main streams of first migrations by origins, destinations and channels

agencies at orig

or dest

student mobility

and recruitment

personal from

origin

personal at

destination

friends and

relatives % N

Germany United Kingdom 36 20 15 24 6 100 52

Slovakia United Kingdom 31 8 11 20 29 100 62

Italy Spain 11 47 9 19 14 100 59

United Kingdom France and

Belgium

20 37 21 20 2 100 51

Spain France and

Belgium

17 37 17 19 11 100 95

Spain United Kingdom 21 30 8 26 16 100 74

Italy France and

Belgium

26 22 35 7 9 100 60

Sweden United Kingdom 15 16 29 27 12 100 52

Ireland United Kingdom 3 29 16 35 17 100 56

Italy United Kingdom 23 25 15 23 14 100 72

Italy Germany 17 19 19 22 23 100 53

Latvia United Kingdom 17 3 17 7 56 100 150

Romania Italy 19 3 5 4 70 100 73

Origin Total %Migration channelsDestination

Data source: YMOBILITY survey, 2015.

The data from the same table allow for the conclusion that it is the origin not the destination

that dictates the most appropriate means to migrate. This is visible in the streams to the UK

but coming from Germany and Slovakia that are mainly forms by youth that reached the

destination through the medium of agencies, not of families or friends. But if you come from

Spain to the UK, the main route or means is education. And, if you come to the UK from the

very close Ireland you do not need agencies, exchange programs or family connections. By

11

The first problem comes from the fact that the number of cases per migration streams between certain origins and certain destinations are rather small, inducing instability in statistical data analysis. Secondly, difficulties of communication in the CATI survey brought significant number of confusing results where people from a certain country are recorded as returning from the same country. The confusion is also favoured by the less and less clear differences between here at origin and there at destination in the context of frequent transnational identifications.

40

distance, history and language, Ireland is very close to the UK and one can find opportunities

there by direct personal search.

Instead of conclusions: from key problems to policy implications

Relative dissatisfactions of returnees are relevant for human costs of migration. The

Romanians and the Irish that returned home by health reasons are among the youth

that paid the highest costs of migration abroad. This is indicated by the very low degree of

satisfaction with life for these categories. The most satisfied with their life are the Swedish

that returned home by business or cumulative reasons. Youth from Germany and Sweden pay

much lower costs for their emigration compared to youth from Latvia, Romania, Slovakia

and Ireland (see table below).

Table 17.People satisfied with their life by survey country and reasons to return (%)

Typology of reasons for return Germany UK Sweden Italy Spain Romania Slovakia Latvia Ireland Total

cumulative upper level motivation 84 90 88 85 69 36 68 56 39 77 business 30 61 87 64 63 67 57 40 40 60accomplished plan 69 54 58 54 56 60 51 52 39 58home 67 70 94 36 56 63 56 43 30 55health 25 54 40 32 58 10 88 82 11 50cumulative middle level

motivation54 54 46 46 47 46 41 47 46 49

home and health 67 17 49 45 42 12 22 33 7 34Total 62 57 54 53 52 50 49 45 42 53

Lowest shares of satisfaction with life for a specific category of reasons are marked by rectangles and

the highest ones by shadow. Reading example: The share of the youth satisfied with their life that

were interviewed in Romania and returned home by health reasons is of 10%, the lowest one in the

series of the nine countries. A similar very low share is recorded for the Irish youth.

The findings above are meant to mark the fact that there are country segments of youth for

witch migration was a failure and returning home is associated with health and family

problems. Such problems are more frequent, at a pan-European level, with the way the

migration process was shaped. Home and health problems as reason for return are also

predictable. They are expected to happen to those that left their country through the

medium of friends and relatives or by personal search from origin, not through the

medium of agencies. The degrees of information and connectedness when leaving the

country predetermine the probability of success or failure at the return (see Table 18).

41

Table 18.Return motivations by channels for the first emigration (%)

agencies at

origin or at

destination

student

mobility and

recruitment

personal

search

from origin

personal

search at

destination

friends

and

relatives

cumulative middle level

motivation

44 35 34 58 60 48

home and health 4 2 9 5 8 5

home 10 3 13 5 11 8

accomplished plan 15 51 21 24 14 25

cumulative upper level motivation 15 4 11 3 2 6

business 7 3 5 4 4 5

health 4 2 8 1 1 3

Total 100 100 100 100 100 100

TotalTyplogy of return motivations Typology of channels for the first migration

The whole causal chain presented above is summarized in the figure 5. Youth from Latvia

and Romania, for example, are more inclined to leaving the country by personal networks

with friends and relatives and this fact contributes to having a rather poor information on

destinations associated , a favourable factor for returning home by health and family

problems. All these are translated , in the end , in higher deprivation after returning.

Usual residence in leaving the country with

Romania or Latvia + the support of friends/relatives

+

returning by health or

family problems -

life satisfaction

at return

-

Usual residence in leaving the country with

Slovakia or Sweden + the support of an agency

The causal chain from residence country to life satisfaction

after the return as indicator of relative migration costs

+ directly proportional relation

- inversly proportional relation

indicators of human costs of migration

Figure 5. Summarising causal patterns of life satisfaction of returnees in for countries

The policy implication of the causal chain starting from family&friends as a major channel

for leaving an emigration country and bringing in the end a certain level of life satisfaction

after return is to recommend policy efforts to facilitate temporary emigration from poor

countries, especially by the use of recruiting agencies more than by personal networks

of family or friends. Better information at the start of the migration process could

reduce human costs of migration. Usually such costs are not included into the

standardised models that treat migration consequences only in terms of economic and

social remittances.

Favouring institutionalised circular migration. The share of repeated or circular migrants

is rather low, of about 6% out of the total sample but it is here, in this category, that is

concentrated the highest level of life satisfaction. At the opposite end, the highest level of

42

dissatisfaction is for the youth that did not migrate but has a structured intention to do it

(table below).

Table 19.Life satisfaction by migration experience (%)

very low low medium high very high

stayers 6 14 31 34 15 100 48,6

high probability potential

migrants

8 16 33 30 14 100 34,8

returnees 6 14 29 36 15 100 6,0

returnees on the move 4 14 27 36 18 100 4,6

circular migrants 5 14 28 35 18 100 2,6

circular migrants on the

move

6 12 28 33 21 100 3,4

Total 7 15 31 33 15 100 100,0

Life satisfactionMigration experience

Total

(row %)

Total

(col. %)

On the move` - with intention of re-migration. Significant association (adjusted standardised residuals)

between column and row values , for p=0.05, are marked by shadow.

One of the possibilities to increasing in a sustainable way life satisfaction by migration,

especially in poor countries, would be by supporting institutionalised circular migration on

specific corridors. The institutionalisation of such a process means a multiplication of

contract forms to allow for regulated circular migration among different countries. This

would involve much better regulations for posted migrants and also enlarged action of

recruiting agencies to facilitate the meeting the demand at destination by labour offer at

origin. An increase in the share of circular migration could contribute, very likely, to a

decrease of permanent or long time emigration that could have consistent negative impacts at

origin.

Origin countries could take advantage of policies recognising that large shares of youth

emigration is not only strictly economical. Migration policies at origin are frequently

considering only the economic side of migration motivation. This could reduce considerably

their efficacy. It is significantly frequent of having also, especially for youth, a much more

complex motivation with evaluating institutions, amenities and lifestyles at home compared

to possible destinations. Key findings from YMOBILITY survey in this area support the

view:

Migration motives for the first migration (Table 6) acts mainly in clusters, grouping

four main spheres of life related to job, education (human capital), personal communities

(networks of friends and family) and amenities (related to services, functioning of

institutions, quality of life, climate etc.).

43

Lifestyle motivation of migration is rather widespread among youth (about 30%) but

manifest mainly in association with job, networks and escaping personal problems. When

associated with job reasons it acts mainly for youth from Romania, Ireland, Latvia and

Slovakia. It is for the case of Germany and Sweden that lifestyle acts in association with

reasons related to solving personal problems by migration.

Job and education reasons are specific for first migrations from the youth from Italy

and Spain.

Even if the surveyed people are all young, age counts a lot in structuring their

motivations. The younger they are, the higher the probability for them to adopt education

networks and lifestyle motivations of migration.

Gender does not seem to a very important motivation for the first migration. More

investigation is needed on the topic with larger samples of returned migrants. With the

existing data one can support the hypothesis that men migrate more than women by

cumulative reasons and women migrate more than men to solving personal problems.

Network migration towards locations of friends and relatives is more frequent for

youth from small rural communities.

The same complexity and significance is also relevant if one considers the self-assessment of

the importance of migration/stability reasons (see annex on migration intentions):

The most important reason for any type of mobility/stability decision is related to

cumulative reasons referring to job, human capital, personal communities and amenities

(quality of services, transparency, climate, style of life etc.).

Job as a family of specific criteria for migration is specific for the youth from

emigration countries plus the youth from Ireland.

Having friends or family at the destination and valuating amenities there is basic in

migration decisions for youth from immigration countries (Germany, Sweden, United

Kingdom) plus Latvia and Ireland.

Youth from Italy and Spain are mainly motivated for their stability/mobility decisions

function of education reasons.

44

Italy and Romania are the only two countries in the survey with significant youth

subgroups valuating to a high degree amenities as a decision criterion for migration/stability.

The majority of the surveyed youth in the nine countries (about 50%) are stayers as they do

not intend to go for work or living abroad in the next one or five years. Medium terms for

leaving, in about the next five years form about one third of the surveyed youth in the nine

countries.

It is only in Romania and Italy that the potential migration is maximum, with shares of

about 48% or 45% (Table A1 in annex). Why Romania and Italy with such high shares of

potential migration ? This could be related to the motivation profile for migration intention.

The dissatisfaction with public administration in the two countries is rather high in

their cases (see table A2 in annex).

Romania has the largest share of potential circular migrants, with intentions to leave in

the next one year and, also, in the next five years (18%). This finding is one of the first

marker of the intensity of circular migration abroad from Romania. Census, vital statistics

and other surveys are very poor in informing us on the topic. It was only the rather low figure

of returned migrants that were recorded with the occasion of the last census in Romania in

2011 (close to 100 thou.) that suggested the hypothesis that circular migration could be a

cause of the low figure at the census moment.

The other clusters of countries by similar patterns of migration intentions are Slovakia-Latvia

(highest share of stayers), Germany-Sweden with a probable maximum share of short term

potential migrants, Spain-Ireland with a profile that is rather close to Italy and Romania, and

the rather unique case of UK with the highest share of stayers.

What else to optimise migration, to favour its course as to be advantageous for

migrants, their families and communities, for sending and receiving countries? Is it

possible to act in such a way even in a rather poor-emigration society? Yes, if principles as

those that are listed below will be implemented:

Well informed and operationalised migration&development strategies are necessary

not only at national level but also for specific categories of migrants and for

decentralised regions of NUTS2 type. To the possible degree, migration policies should

be integrated with development policies. It is not only for professional categories that such

45

strategies are necessary but also for age and gender categories. Governments per se, without a

decentralised administration will be, very likely, unable to design and implement such

strategies.

Especially in the emigration countries (Romania, Latvia and Slovakia), a series of

institutional changes that do not counts only for migrants are necessary as to modernise the

local and central public administration, increase the competitiveness of economic

companies to make the country more attractive especially for youth.

It is also useful to laying the ground for designing such policies by organising large

surveys in diaspora communities abroad, on migration topic at home, and also by

supporting the foundation of an Observatory of East-West European migration.

There is a high diversity of reasons to migrate and to return among countries. Various

analyses bring the conclusion that the nine countries from YMOBILITY survey cluster not so

much by homogeneous groups. There is more convincing evidence on networks of

similarity among motivation profiles. The nuclei of similarity within these networks are

formed by the youth from Latvia, Romania and Slovakia, on the one hand, and Germany-

Sweden-Spain on the other hand . The finding supports one of the hypotheses of the research

on the higher similarity of migration motivation within the grouping of the emigration

countries from the Central and Eastern Europe. Slovakian profile of the youth migration is

not all the time very close to those of Romanians and Latvians. Their return migration profile

is closer to those of the Italian Youth.

Irish youth show a higher similarity with British youth on return motives but is closer to the

Romanian youth on motives for the first migration.

The most accurate measurement that uses a large number of control variables (Table 7) fully

supports the idea that first migration motivations are highly different by countries. The youth

from Latvia, Slovakia and Romania is no more a homogeneous cluster. The specific (net)

country effects is visible for each of them or for small subgroups on different dimensions:

Romanians, like Irish youth, are significantly more in the category of lifestyle and job

motivation for the first migration; Latvian and Slovakian youth has no more specific

motivation for the first migration when control variables are at work; Germany and Sweden

youth are similar for their high probability of emigration for escaping personal problems and

getting new styles.

46

Motivation profiles for return migration are sharply contrasting with those of emigration.

Segments of youth from different countries are much more similar on return than on first

migration motivation (Table 11). It is here that the starting hypothesis (H2) is full supported:

the impact of living in Latvia, Romania and Slovakia has a very high net effect on adopting

family or family and health reasons for returning home; Germany and Sweden are obviously

highly similar by the motivation profiles of their youth to retouring home (high frequencies

on the categories of reasons related to family, family and health, and accomplished plans); the

Spanish and the Italian youth are close on their return reasons to the profile that is specific for

the youth from Germany and Sweden; the Irish profile of return motivation has no specificity,

being closer to the British profile .

Gender is a rather weak predictor for the first migration motivation (Table 7) compared to its

impact on return migration (Table 11). Having a larger sample, would, very likely, contribute

to supporting the hypotheses that young women are more oriented to emigrate temporarily for

education, networks and lifestyle and to escaping personal problems at origin.

Return migration by family and health reasons is more likely for the young women (caeteris

paribus, as in any multiple regression model). Young mean are more oriented to return home

by reasons related to health, business and cumulative reasons.

Putting together reasons to migrate and means to do it one can easily note the central place of

gender into the causal web of the migration process. The typical young woman from the

YMOBILITY survey, for example, emigrates for education or for escaping personal

problems at the local level and getting access to a new style of life. And she reaches these

targets by friends, relatives of by personal search of opportunities at destination.

Emigration and return motivations and migration channels are significantly regionalised.

Youth from less developed regions, for example, have a higher probability of returning home

for business and for family and health reasons (Table 11). Those coming from more

developed regions (Table 16) are more oriented to use as means of migration labour

recruiting agencies or to do personal search of opportunities at destination.

Migration reasons and the means to accomplish migration are in interaction. Sometimes

reasons determine the efforts to accumulate the means and in other situations the availability

of means creates the motivation for migration.

47

The YMOBILITY survey investigated youth migration experiences and their determinants at

individual, regional and country level. Further explorations are needed to better understand

the variations of the causal patterns of youth migration at the levels of migration streams

between countries, origin regions and destination countries and, also, at the level of migration

streams among regions. The small subsamples that we had into this survey clearly show

that the means for reaching different destinations largely vary function of the migration

stream (Table 18).

48

Annex on migration intentions12

Table A 1.How structured are intentions to migrate abroad in the next five years

Cells that are marked by indicate significant positive associations (adjusted standardised

residuals, for p=0.05).

Table A 2.` In any decision that you make about migrating or staying what is the importance

of the following reasons?`

Figures marked by shadow indicate positive associations (adjusted standardised residuals) between

the survey country and the row category of reasons. Example: 18% out of the total interviewed

Slovakians declare that family and friends are important factors for their stability/mobility residential

decision. This percent is significantly higher than expected by the pure chances within the total

sample.

12

Dumitru Sandu

Reasons that are

important for

migration/stability (cultural

patterns of migration

motivation)

Germany Sweden UK Ireland Romania Latvia Slovakia Italy Spain Total

cumulative reasons 25 22 32 32 40 37 26 29 28 29

job 9 9 8 14 15 13 13 8 9 10

amenities 7 9 7 10 17 5 8 23 10 11

amenities, friends and

familiy 16 17 18 20 10 17 10 7 6 12

friends and family 13 12 12 11 7 14 18 8 17 12

human capital investment 5 7 4 4 5 4 9 10 15 8

non of the above are

important on the topic25 24 19 10 5 11 16 15 14 17

Total 100 100 100 100 100 100 100 100 100 100

AMENITIES – public services, transparency, housing, health, company, quality of life,

climate, lifestyle

JOB – employment, career, salaries, jobskills

HUMAN CAPITAL INVESTMENT – language skills, education, language barriers

FAMILY&FRIENDS - being with friends, being with my family

How the criteria of

importance for the ideology

on migration/stability are

grouping on the whole

sample

no

intention

unlikely undecided likely arrangements

done

Romania 18 11 23 31 17 100

Italy 17 15 23 33 12 100

Ireland 25 14 22 27 12 100

Spain 24 15 22 28 11 100

Germany 25 18 22 25 10 100

Slovakia 26 19 23 23 9 100

Latvia 28 22 21 20 9 100

Sweden 29 16 24 21 11 100

UK 31 18 24 17 10 100

Total 25 16 23 25 11 100

Intention to migrate or to return home for the next 5 years (%)

Data source: YMOBILITY survey, 2015. Weighted data. N= 29677

To

tal

49

Table A 3.Intention motivations for high structured potential migration

of those intending to leave in the next five years

Figure A 1. Similarity on motivations to migrate or stay, by countries:

youth of high structured intentions to migrate

Germany Sweden UK Ireland Slovakia Latvia Romania Italy Spain

Salaries 67 64 68 73 75 75 71 68 69

Being with my family 53 56 57 53 59 58 46 32 52

Language barrier 33 26 43 32 39 36 29 33 45

Employment prospects 73 73 71 73 67 69 70 70 72

Housing opportunities 64 71 56 49 59 60 54 51 47

Healthcare 60 58 56 52 44 51 52 45 38

Climate 49 53 46 44 27 39 28 37 31

General welfare 63 65 73 67 60 65 66 65 59

Lifestyl or culture 50 49 59 59 41 39 49 47 45

Being with friends 35 33 35 36 38 29 24 23 30

Acquiring new job skills 49 48 47 59 66 53 63 63 58

Corruption 40 40 43 42 33 36 52 54 45

Education reasons 31 41 33 39 48 35 50 50 56

Career advancement opport. 58 55 57 64 57 64 62 61 69

Public services 41 33 45 46 36 48 44 52 41

Improving language skills 61 51 38 35 61 56 50 62 65

Company policy 26 33 23 28 39 36 41 37 28

Criteria for migration/stability NORDIC MODEL SOUTH-EASTERN MODEL Figures in the table are standardized percentages (Hull scores) by columns of persons declaring that the reference reason is important for their decisions. The mean for each column is 50 and the standard deviation is 14. Shadow indicare highest values on rows.

Reading example: the youth from Germany, of

high structured intentions to live abroad in the

next one or five years, have patterns of

motivation that are very similar to those from

Germany and Sweden. Results of cluster

analysis using furthest neighbour method and

squared Euclidean distances, standardized

variables. Input data are percentages of youth

having structured intentions to live abroad in

the next one or five years, at country level, by

17 reasons of migration/stability. Total

N=12708, weighted data.

The Romanian youth with well structured

intentions to migrate are most similar with the

youth from Latvia from this point of view. The

situation is the same even if one considers the

youth without structured intentions to migrate

(see next table)

50

Table A 4.Young Romanians on what are the important reasons for them to stay or migrate

abroad, by how structured are their intentions to leave the country (%)

structured

intentions to

migrate

unstructured

intentions to

Salaries 89 89

Employment prospects 88 88

General welfare 85 85

Acquiring new job skills 83 80

Career advancement opportunity 82 79

Healthcare 73 70

Housing opportunities 71 75

Lifestyl or culture 70 66

Improving language skills 70 70

Education reasons 69 70

Language barrier 51 49

Corruption 69 73

Public services 65 62

Being with my family 64 68

Being with friends 44 46

Company policy 60 62

Climate 47 50

NETWORK

CAPITAL

OTHER

Spheres of

life for

motivation

Young Romanians withReasons to migrate or stay home

JOB AND

INCOME

HEALTH AND

HOUSING

CULTURE

AND

LIFESTYLE

PUBLIC

SPHERE

Original question: ”In any decision that you make about migrating or staying what is the importance of the following reasons..”. Answers on five point scales, recoded to have very important and important as 1 and 0 as other. The intention to migrate was recorded for the next year and , separatly, for the next five years. An intention was considered to be structured if the person declared that she/he has concrete arrangements to leave or that it is ”it is very likely to leave”.

• Job and income are the main reasons for the intentions to migrate of the young Romanians.

• Some of the motivations increase in their relevance if one moves from unstructured to structured intentions to migrate. This is the case for lifestyle, healthcare and career advancement.

51

References

Benson M., O'Reilly, K. 2009. Lifestyle migration: Expectations, aspirations and

experiences: Ashgate Publishing, Ltd.

Clarke A. 2005. Situational analysis: Grounded theory after the postmodern turn: Sage.

Clark, W. A. 2013. Life course events and residential change: Unpacking age effects on the

probability of moving. Journal of Population Research, 30(4), 319-334.

De Jong G. F., Fawcett J. T. 1981. Motivations for migration: an assessment and a value-

expectancy research model. In: De Jong GF, Gardner RW, ed. Migration decision

making: multidisciplinary approaches to microlevel studies in developed and

developing countries. New York, Pergamon, 1981: 13-58. (Pergamon Policy Studies

on International Development).

De Jong G. F. 2000. Expectations, gender, and norms in migration decision-making.

Population Studies, 54(3): 307-319.

King, R., & Williams, A. M. 2018. Editorial introduction: New European youth mobilities.

Population, Space and Place, 24(1), e2121.

Klabund A., Willekens F. 2016. Decision-Making in Agent-Based Models of Migration:

State of the Art and Challenges. Eur J. Popul, 32(1): 73-97. doi:10.1007/s10680-015-

9362-0

Michael Beenstock, P. R. R., Professor Jordi Suriñach, D., & Royuela, V. (2015). The role of

urbanisation on international migrations: a case study of EU and ENP countries.

International Journal of Manpower, 36(4), 469-490.

Pahl R., Spencer, L. 2004. Personal communities: not simply families of ‘fate’or ‘choice’.

Current Sociology, 52(2): 199-221.

Pettigrew T. F. 2015. Samuel Stouffer and relative deprivation. Social Psychology Quarterly,

Vol. 78(1) 7–24.

Sandu D. 1988. Developments in k-linkage clustering for sociology. Revue Roumaine des

Sciences Sociales, 32(1): 31-43.

Sandu D., De Jong G. F. 1996. Migration in market and democracy transition: Migration

Intentions and Behavior in Romania. Population Research and Policy Review, 15(5-

6): 437-457.

Sandu D., Tufis P. 2016. Youth migration as a strategic behaviour in a multilevel approach.

[Working Paper in the YMOBILITY series].

Sandu, D. 2016. Destination choices of Romanian migrants abroad in times of crisis. Paper

presented at the XVI International Congress of Migration, Almeria - Spain, March 10-

12, 2016, Almeria University, Spain.

Sandu, D., Toth, G., & Tudor, E. 2018. The nexus of motivation–experience in the migration

process of young Romanians. Population, Space and Place, 24(1), e2114.

Schutz A., Embree, L. 2011. Collected Papers V. Phenomenology and the Social Sciences:

Springer Verlag.

Sørensen, N. N.,2012.Revisiting the migration–development nexus: From social networks

and remittances to markets for migration control.International Migration,50(3),61-76.

Stark O., Taylor J. E. 1991. Migration incentives, migration types: The role of relative

deprivation. The Economic Journal, 101(408): 1163-1178.

Treiman, D. J. (2014). Quantitative data analysis: Doing social research to test ideas: John

Wiley & Sons.Clark,

Williams, A. M., Jephcote, C., Janta, H., & Li, G. (2018). The migration intentions of young

adults in Europe: A comparative, multilevel analysis. Population, Space and Place,

24(1), e2123.

52

Zelinsky, W. 1971. The hypothesis of the mobility transition. Geographical Review, 219-249.

Documents

Dumitru Sandu*, Paula Tufiș* · Dumitru Sandu*, Paula Tufiș* *University of Bucharest 1 The text, with slight improvements, is part of the authors’ contribution to the WP4.1 (section

Dumitru Sandu, Paula Tufiș · Dumitru Sandu, Paula Tufiș *University of Bucharest 1 The text, with slight improvements, is part of the authors’ contribution to the WP4.1 (section