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Abstract

Personality traits are influential in individual decision-making but have been overlooked in economic models of migration. This paper investigates the relation between Big Five personality traits and individuals’ migration intentions among alternative destinations that vary in their culture distance. We hypothesize that Big Five personality traits may alter individuals’ migration decision and destination choice through their influence on perceived psychic costs and benefits of migration. We test our hypotheses using the Fachkraft survey conducted among university students in Germany. We find that extraversion and openness are positively associated with migration intentions, while agreeableness, conscientiousness, and emotional stability negatively relate to migration intentions. We show that openness positively and extraversion negatively relate to the willingness to move to culturally distant countries even when we control for geographic distance and economic differences between countries. Using language as a cultural distance indicator provides evidence that extravert individuals are less likely to prefer linguistically distant countries while agreeable individuals are more inclined to consider such countries as alternative destinations.
Researchcentrum voor Onderwijs en Arbeidsmarkt | ROA
Research Centre for Education and the Labour Market | ROA
ROA
ROA-RM-2018/7
Personality traits, migration intentions,
and cultural distance
Didier Fouarge
Merve Nezihe Özer
Philipp Seegers
ROA Research Memorandum
Personality traits, migration intentions,
and cultural distance
Didier Fouarge
Merve Nezihe Özer
Philipp Seegers
ROA-RM-2018/7
December 2018
Research Centre for Education and the Labour Market
Maastricht University
P.O. Box 616, 6200 MD Maastricht, The Netherlands
T +31 43 3883647 F +31 43 3884914
secretary-roa-sbe@maastrichtuniversity.nl
www.roa.nl
Abstract
Personality traits, migration intentions, and cultural distance*
Personality traits are inuential in individual decision-making but have been overlooked
in economic models of migration. This paper investigates the relation between Big Five
personality traits and individuals’ migration intentions among alternative destinations
that vary in their culture distance. We hypothesize that Big Five personality traits may
alter individuals’ migration decision and destination choice through their inuence
on perceived psychic costs and benets of migration. We test our hypotheses using
the Fachkraft survey conducted among university students in Germany. We nd that
extraversion and openness are positively associated with migration intentions, while
agreeableness, conscientiousness, and emotional stability negatively relate to migration
intentions. We show that openness positively and extraversion negatively relate to the
willingness to move to culturally distant countries even when we control for geographic
distance and economic differences between countries. Using language as a cultural
distance indicator provides evidence that extravert individuals are less likely to prefer
linguistically distant countries while agreeable individuals are more inclined to consider
such countries as alternative destinations.
JEL classication:D91, J61, Z1
Keywords: migration intentions, destination choice, cultural distance, Big Five
personality traits
Didier Fouarge
Maastricht University
ROA
P.O. Box 616
NL-6200 MD Maastricht
The Netherlands
d.fouarge@maastrichtuniversity.nl
and NETSPAR and IZA
Merve Nezihe Özer
Maastricht University
Department of Economics
P.O. Box 616
NL-6200 MD Maastricht
The Netherlands
m.ozer@maastrichtuniversity.nl
Philipp Seegers
Candidate Select GmbH (CASE)
Raderberger Str. 173-175
50968 Cologne
Germany
p.seegers@maastrichtuniversity.nl
and Maastricht University
* We gratefully acknowledge comments from Frank Cörvers, Davey Poulissen, and seminar participants at the
XIX Applied Economics Meeting 2016 in Seville. The views expressed herein are those of the authors and do not
necessarily reect the views of institutions that authors are afliated.
1
1 INTRODUCTION
Labor migration is theorized in neoclassical economic literature as an investment decision driven by human
capital characteristics of individuals and expected wage gains (Massey et al., 1993). Despite their significant
impact on individuals’ decision to migrate, these factors are not sufficient to explain why some individuals
migrate while others do not even if they share the same prospects for economic gain and socio-demographic
characteristics. This is because the decision to migrate is a complex process that is also influenced by non-
economic factors such as cultural differences (Belot and Ederveen, 2012) and individuals’ perception of
potential costs and benefits of migration. These perceptions are shaped by preferences (Bauernschuster et
al., 2014; Czaika, 2012; Groenewold et al., 2012) and psychological dispositions (Fawcett, 1985).
Personality traits are influential in a large array of economic decisions (Becker et al., 2012) but have been
overlooked in economic models of migration. This paper contributes to this thin literature by investigating
the relation between personality traits and individuals’ intentions to migrate to culturally different
alternative destinations.
This paper hypothesizes that individuals’ personality traits affect the way they weigh the psychic costs and
benefits of migrating to alternative locations. Alternative locations differ in their economic and non-
economic characteristics. The attractiveness of rich and well-developed regions is well documented in
literature (Bertoli et al., 2013; Mayda, 2010; Pedersen et al., 2008). Culture, is one of the non-economic
dimensions influencing the attractiveness of alternative destinations for potential migrants (Bauernschuster
et al., 2014; Belot and Ederveen, 2012; Wang et al., 2016). However, little is known about how cultural
differences are subjectively evaluated by individuals with different personalities. For example, one could
expect that individuals scoring high on openness to new experiences (one of the Big Five personality traits)
have a more positive perception of the net benefits of migrating to culturally more remote regions.
1
Answering this question is important to gain insights about how immigrants are self-selected and sorted
into alternative destinations, and the implications of this sorting for the integration in culturally different
environments.
To the best of our knowledge, Ayhan et al. (2017) and Bütikofer and Peri (2017) are the only papers
addressing the role of personality in migration decisions with an economic outlook. Ayhan et al. (2017)
found that openness is positively associated with higher propensity of migration from rural to urban areas
while conscientiousness is negatively related to rural-urban migration in Ukraine. They also find a negative
1
Definitions of personality traits used in literature vary. We focus on the Big Five taxonomy in which personality is broken down
into five main dimensions: extraversion, agreeableness, conscientiousness, emotional stability, and openness to new experiences
(Goldberg, 1992). Our data includes measurements of personality traits according to this taxonomy.
2
relation between extraversion and propensity to migrate from rural areas to cities. Bütikofer and Peri (2017)
analyzed the migration patterns of Norwegian male population born in 1932-1933 enlisted for military
service using two non-cognitive skills called adaptability and sociability and found that adaptability skills
have a strong impact on migration. Although the latter of these two studies included an analysis for
emigration from Norway, both focused on internal migration and treat location choice as a preference over
different administrative units within a country.
Migration psychology literature provides a more extensive treatment on the relation between personality
traits and migration. There is a consensus in this literature on the positive association between migration
and openness and extraversion (Camperio Ciani et al., 2007; Canache et al., 2013; Jokela, 2009; Jokela et
al., 2008; Paulauskaitė et al., 2010; Silventoinen et al., 2008). However, evidence for other traits is
ambiguous. Paulauskaitė et al. (2010) found a negative relation between conscientiousness and the intention
to emigrate, and Jokela (2009) did not find such a significant association. Similarly, Paulauskaitė et al.
(2010) did not find a relationship between the intention to migrate and agreeableness while Jokela (2009)
showed that less agreeable individuals are more likely to migrate. Moreover, Huang et al. (2005) found that
agreeableness is positively associated with adaptation to local community once migration occurred. For
neuroticism, Silventoinen et al. (2008) and Jokela et al. (2008) found a positive relationship with the
intention to migrate while Jokela (2009) did not find a significant association.
Although migration decisions involve the choice of where to move, fewer studies addressed the potential
role of personality traits on location choices. Jokela et al. (2008) found that highly sociable (i.e., extravert)
individuals are more likely to migrate longer distances and to prefer urban areas while highly emotional
(i.e., neurotic) individuals tend to migrate shorter distances. Murray et al. (2005) found that individuals
living in highly accessible locations in Australia (where opportunities for social interaction and services are
more abundant) have higher levels of openness and extraversion. In these papers, either preferences over
different administrative units to live or geographic distance is used as proxies of location choice. However,
these proxies do not fully capture the potential costs associated with migrating to culturally distant
locations. To the best of our knowledge, we are the first to investigate the association between personality
traits and the perception of alternative destination countries based on cultural distance.
We test the hypothesis that personality traits are related to the migration decision using the Fachkraft data
gathered among students at German universities in March 2015. Students were asked whether they want to
work abroad after they graduate and, if yes, in which country.
2
The survey also includes a fifty item IPIP
Big Five personality test by Goldberg (1992). We estimate two models to test the relation between the
2
This means we define migration as voluntary labor migration in this study.
3
various facets of personality and students’ migration intentions and their preferences over alternative
destinations that we characterize based on cultural distance. We construct a measure of cultural distance
using Hofstede national culture dimensions indicating cultural difference between Germany and the
countries students prefer to work. Our results show that being more extravert and open to new experiences
is associated with stronger intentions to migrate while being more agreeable, conscientious, and emotionally
stable is correlated with lower migration intentions. We show that openness positively and extraversion
negatively relate to the willingness to move to countries culturally more remote, even when we control for
geographic distance and economic differences between countries. Our robustness checks using language
distance show that extraverts are significantly less likely to prefer countries where German and English are
not official languages, and that more agreeable students are more likely to consider these countries as
alternative destinations.
The paper is structured as follows. Section 2 provides a conceptual framework for our hypotheses. Our data
and estimation strategy are introduced in Section 3 and Section 4, respectively. Section 5 presents the
estimation results. Section 6 provides a discussion of our findings and concludes the paper.
2 CONCEPTUAL FRAMEWORK
Economic theory suggests that individuals decide to migrate by comparing their expected lifetime utility in
their current location with that in alternative destinations net of costs associated with their location decision.
We conceptualize the role of personality traits in this cost-benefit analysis. In doing this, we follow the line
of reasoning provided by Almlund et al. (2011) and Borghans et al. (2008) who suggest that personality
can be incorporated into the individual decision mechanism through constraints, preferences, and
expectations.
Individuals differ in their personality traits which may lead to different constraints (Borghans et al., 2008).
Having certain personality traits may constitute a constraint by affecting the costs associated with migration
decisions which leads individuals to make different migration decisions and choose different locations.
Migration involves both monetary and non-monetary costs that differ across alternative destinations.
According to Sjaastad (1962), monetary costs represent the out-of-pocket money spent for traveling and
relocation and costs of gathering information. Such costs depend on socio-economic characteristics such as
education and cognitive ability.
3
Non-monetary costs involve psychic costs due to leaving a familiar
3
Individuals with high level of ability and education might have lower cost of gathering information and higher chance of obtaining
a visa or residence permit. Schwartz (1973) showed that the negative impact of distance on migration decreases with education and
interpreted this finding as the indication of informational costs being lower as skill levels increase. However, it should be noted
that monetary costs can also be indirectly affected by personality traits via their impact on individual outcomes such as educational
4
surrounding behind, building up new social relations abroad, and adaptation to a new social and cultural
environment. Such costs can be determined by the same factors that affect monetary costs. For example,
Bauernschuster et al. (2014) found that highly educated individuals more easily adapt to culturally different
environments than those with lower educational attainments. We hypothesize that non-monetary costs are
a function of personality traits. The level of psychic costs may vary across individuals since they may
differently perceive these costs due to their particular personality traits.
Preferences and expectations are the other two channels through which personality traits may affect
migration decision and location choice.
4
If having specific traits make individuals less risk averse or less
impatient, then those traits may lead to a higher likelihood of migration.
5
Furthermore, migration decisions
depend on how expectations about potential outcomes in alternative locations are constructed. Formation
of expectations is based on how individuals perceive and process information which is affected by
personality traits in different ways (Almlund et al., 2011). For instance, people more open to new
experiences can gather more information (Almlund et al., 2011). Depending on their personality and how
they construct their information set, individuals may well predict, inflate, or deflate the benefits expected
to be obtained in alternative locations which, in turn, may affect their decision.
Considering that personality traits enter into the decision mechanism via expected benefits and/or perceived
costs, we expect the following relations between the decision to migrate to culturally distant destinations
and the Big Five personality traits:
Extraversion Extravert individuals are described by characteristics such as being talkative, sociable,
enterprising, adventurous, and optimistic (Goldberg, 1990). Moving to another place means a person’s
leaving her social network behind and building up a new network in the new location. Thus, being sociable,
talkative, and enterprising makes it more likely to be more willing to migrate to new social circles.
Furthermore, being optimistic may make extraverts confident about their potential outcomes in the new
location as they may tend to be overconfident in assessing their performance in tasks (Schaefer et al., 2004).
In this respect, extraverts are expected to be more likely to migrate and this has been found in several studies
(Canache et al., 2013; Jokela, 2009; Jokela et al., 2008; Silventoinen et al., 2008). Jokela et al. (2008) found
that high sociability is related to moving to urban areas and longer distances although they did not
attainment. See Almlund et al. (2011) for a review of studies on predictive power of personality on education outcomes and earnings
capacity.
4
On the relation between risk preference and migration decisions, see Massey (1990) and Jaeger et al. (2010). For the relation
between time preference and migration decisions, see Bowles (1970) and Nowotny (2014)
5
As personality shapes preferences, preferences may also shape personality. Although there is no evidence on the direction of
causality, literature provides correlational evidence on the relationship between personality traits and economic and social
preferences. See Almlund et al. (2011) for a review.
5
distinguish geographic and cultural distance. Because extraverts are more adventurous and optimistic, this
could result in the fact that they perceive the psychic costs of migration to be lower or the expected utility
to be higher in case of moving to a culturally distant destination.
Agreeableness This trait refers to characteristics such as being friendly, respectful, adaptable, and flexible
(Goldberg, 1990). Jokela (2009) showed that more agreeable individuals are more likely to have strong ties
within their community. As agreeable individuals tend to internalize the values and norms of their local
community, this makes them less likely to migrate. However, once they decide to migrate, more agreeable
people can perceive the psychic costs of moving to culturally distant regions to be lower. Huang et al.
(2005) showed that more agreeable expatriates better integrate to the local community in their destination
country. Hence, there are two opposing effects in the relation between agreeableness and migration. On the
one hand, agreeable individuals may perceive costs of leaving their community behind to be higher and
therefore be less likely to migrate. On the other hand, they may perceive psychic costs to be lower once
they start to live in a different location, as they are more flexible and adaptable to other cultures.
Conscientiousness This trait is characterized by being organized, systematic, responsible, predictable, and
conventional (Goldberg, 1990).Conscientious individuals, just like extraverts, tend to be overconfident in
assessing their performance (Schaefer et al., 2004). Although this characteristic is expected to make them
predict their expected utility in an alternative location to be higher, other traits associated with
conscientiousness may decrease the likelihood of such individuals to migrate. As predictability and order
are important to them, conscientious people may perceive the psychic costs of migration to be higher as it
involves uncertainties. Moreover, Paulauskaitė et al. (2010) argued that conscientious individuals are less
willing to migrate as they may feel more responsible for their family and community. Therefore, we expect
to find a negative association between conscientiousness and willingness to migrate. Huang et al. (2005)
suggested that more conscientious expatriates are more likely to experience difficulties with integration as
they perceive the new environment to be unpredictable. As cultural dissimilarity increases, unforeseen
circumstances a potential migrant may experience also increase. Hence, if they migrate, conscientious
people are expected to migrate to destinations that are culturally similar to their region of origin.
Emotional stability This trait is associated with characteristics such as being calm, peaceful, balanced, and
confident. Neuroticism, the opposite of emotional stability, is related to being anxious, nervous, fearful,
and negativistic (Goldberg, 1990). At first sight, it seems that emotionally stable individuals may be more
likely to migrate as being stable and confident may make them better able at dealing with uncertainties
associated with migration. However, Silventoinen et al. (2008) and Jokela et al. (2008) found a positive
relation between neuroticism and migration. These findings may be driven by neurotic people having lower
6
job satisfaction (Van Den Berg and Feij, 1993) and lower neighborhood satisfaction (Jokela et al., 2008).
Hence, the sign of the relation between emotional stability and migration is hard to predict. Furthermore,
Jokela et al. (2008) found that higher neuroticism is correlated with a lower geographical distance migrated.
The authors hypothesized that neurotics may avoid long distance migration due to their tendency to feel
distressed. In terms of cultural distance, two opposing effects can be expected. If proneness to anxiety and
fear is dominant in neurotics, then emotionally stable individuals are expected to move to culturally more
distant regions compared to neurotic individuals. However, if dissatisfaction with current location prevails
in neurotics, then emotionally stable individuals may be less likely to move to culturally distant regions.
Openness to new experiences Individuals who are open to new experiences are characterized by being
inventive, curious, and cosmopolitan (Goldberg, 1990). As migration is essentially an experience full of
novelty in terms of location, social networks, and culture, open individuals are expected to be more willing
to experience it. As in other studies (Canache et al., 2013; Jokela, 2009; Paulauskaitė et al., 2010), we
therefore expect to find a positive association between migration and openness. It is also straightforward to
expect a positive correlation between cultural distance and openness for at least two reasons. First, because
open individuals are more curious, they may search more and construct a more accurate information set
(Almlund et al., 2011) leading them to more accurately predict their utility in a different location. Second,
because open individuals are curious about novelties, they may perceive psychic costs of adaptation to be
lower as culturally different locations may be even more attractive to them.
In brief, we expect more extravert, less agreeable, less conscientious, and more open individuals to be more
likely to report migration intentions. Furthermore, we expect more extravert, more agreeable, less
conscientious, and more open individuals to move to culturally distant locations as individuals having these
traits may either consider a broader choice set when making their decisions or predict a higher expected
utility in case of moving to an alternative location. We do not have a clear prediction for emotional stability
as the results depend on which of the opposing effects mentioned above dominates.
3 DATA
We use the Fachkraft data to test our hypotheses. It is a biannual survey conducted by Maastricht University
in cooperation with Studitemps GmbH among students at German universities. The survey aims to gather
information on general study characteristics, the part-time student job market, and students’ future career
expectations. Data is gathered online through ‘Jobmensathat is the largest student network in Germany
for student jobs and internships and has more than 400,000 users. Questionnaires are filled in via the survey
hosting service called ‘FluidSurveys’. Data collection started in September 2012. We used the data from
7
round six conducted in March 2015. University students using Jobmensa received an invitation via e-mail
to participate to the survey. 7% of these students participated to the survey in March 2015.
6
61% of them
completed the main questionnaire. Although participation is incentivized, the response rate is low.
Nevertheless, the sample is representative for the student population: the distribution of observable
characteristics in the Fachkraft data does not differ substantially from the Sozialerhebung, another large-
scale German survey among students having a systematic sample and conducted regularly at German
universities by the government (Bergerhoff et al., 2015).
The Fachkraft survey includes a question on where university students want to work after their graduation.
7
Students are provided with a binary response option where they can choose either Germany or abroad. Our
first outcome variable, intention to migrate, is constructed based on the responses given to this question.
This variable reflects stated preferences of students rather than their actual behavior. There are different
standpoints across disciplines on how intentions relate to actual behavior. Intentions are considered as an
integral part of decision making process in sociological and psychological theories of mobility (DaVanzo,
1980; Fawcett, 1985). This strand of literature assumes sequential decisions for mobility where the intention
to move is followed by actual move (Lu, 1999). In economics, research traditionally focuses on actual
behavior rather than intentions. This is because individuals’ preferences are believed to be revealed by their
actual behavior but not to be fully reflected by their intentions (DaVanzo, 1980). Nevertheless, the use of
stated preferences in several subfields of economics has become common as stated preferences allow to
simulate market setting and to model choices by fully observing the alternatives (Sund, 2010). According
to the theory of reasoned action by Fishbein and Ajzen (1975), acting depends on the intention to act which
is determined by beliefs about and evaluation of the consequences of acting and one’s motivation to comply
with these beliefs. Especially international migration is a complex process, which requires extensive
preparation to gather information regarding the destination country, to find a job and an accommodation,
and to deal with bureaucratic processes such as obtaining a visa or residence permit. In this respect, intention
to migrate may indicate future actual migration if it includes motivation to prepare for it. While intentions
are informative for actual behavior, research shows that there is no one-to-one correspondence between
intention to migrate and actual migration. Van Dalen and Henkens (2013) found that 34% of native Dutch
residents who stated their willingness to emigrate actually moved abroad in the following five years after
their first survey. Thus, we should note that our results should not be directly translated to realized
migration.
6
There are 28,064 participants in total but almost 16% of them are either high school students or persons who already graduated
from university. We focus on 23,585 university students to obtain a homogeneous sample.
7
The question is “Where would you like to work after the study?” (original question in German: “Wo wollen Sie nach dem Studium
gerne arbeiten?”).
8
Students who stated their intention to work abroad are also asked which country they would like to move
to. Using this information, we constructed our second dependent variable, cultural distance, as the cultural
difference between Germany and the country of migration students indicated. Hofstede (2001) defines
culture as ‘collective mental programs’ reflected by values and behaviors of individuals living in a society
which differentiate them from the members of another society. Hofstede’s initial four-dimensional
taxonomy
8
is based on a survey on values conducted among employees of the International Business
Machines (IBM), a large multinational company, around the world between 1967 and 1973. Since then the
survey (recently called Values Survey Module) has been conducted in many other countries and the most
recent data is published on Hofstede’s website. Hofstede’s national culture dimensions are a standard in
literature, and used in many research fields in psychology, sociology, international marketing, and
management (Søndergaard, 1994; Steenkamp, 2001). We think that the Hofstede framework provides an
appropriate measure of cultural difference for our study. Hofstede and McCrae (2004) showed that Big Five
personality traits are correlated with national culture dimensions. Their findings indicate that individuals’
personality is to a certain extent linked to the ‘collective mental programs’ of the societies they live in. In
this respect, the deviation of an individual’s personality traits from the average traits observed in a society
may be a good predictor of how much a person is likely to move to culturally distant countries.
Following Kogut and Singh (1988), we compute the cultural distance between home country , Germany,
and preferred migration country as follows:



(1)
where  represents the score of a country in each culture dimension     ,  represents Germany’s
score in that dimension, and is variance of the scores in dimension . This index measures the deviation
of every alternative destination country from Germany in each Hofstede dimension. Then deviations are
corrected for differences in the variance of dimensions to equalize the scale across dimensions for
averaging.
8
The initial Hofstede taxonomy includes the following dimensions: (i) Power distance index (PDI) expressing to what extent the
less powerful individuals in a society expect and accept the unequal distribution of power. This dimension reflects the level of
hierarchy in a society. (ii) Individualism index (IDV) measuring the degree to which individuals are responsible only for themselves
and their immediate family in a society. The counterpart of it is collectivism where individuals are seen as an integral part of larger
groups. (iii) Masculinity index (MAS) reflecting the distribution of emotional roles between men and women in a society. (iv)
Uncertainty avoidance index (UAI) expressing to what extent the members of a society tolerate unexpected and unstructured
situations. Later, two other dimensions are also added which are long-term vs. short-term orientation and indulgence vs. restraints.
The detailed descriptions for all dimensions can be found in Hofstede (2001) and Hofstede and Hofstede (2015, December 08). We
did not include the last two dimensions when constructing our cultural distance variable since index values for these dimensions
are only available for a limited number of countries.
9
The Fachkraft survey includes the fifty item IPIP Big Five personality test based on Goldberg (1992) and
Goldberg et al. (2006). Participating into this part of the survey is optional. 52% of students who responded
the main part of the survey also participated to the personality test. Our key independent variables are the
students’ scores in five dimensions of personality constructed as follows: There are ten items for each
personality traits consisting five ‘positive keyed’ and five ‘negative keyed’ items that represent two poles
of a trait.
9
Students are asked to assess to what extent a given item reflects their personality on a five-point
Likert scale ranging from very inaccurate to very accurate. This scale is scored from one to five for positive
keyed items and from five to one for negative keyed items. We obtained students’ total scale score by
summing all score numbers assigned to each item in the test.
10
In our analysis, we use students’ personality
scores standardized to mean 0 and standard deviation 1. Besides, we included a range of control variables
in our analyses that we selected based on the previous migration literature.
11
Table A1 provides the
description of our dependent and independent variables.
13% of university students do not have a German passport. Foreign students studying in Germany might
be willing to return to their home countries after they complete their study program. For this reason, we
restricted our analysis to students who have a German passport.
12
Our final estimation sample includes
8,572 students. Summary statistics for all variables are provided in Table A2. Table A3 provides further
descriptive statistics for students’ preferred destinations and their geographic, economic, and cultural
distance from Germany.
Table A4 shows the mean personality traits of students who intend and do not intend to migrate. As
expected, students who have an intention to move abroad are more extravert, less agreeable, less
conscientious, less emotionally stable, and more open compared to students who have the intention to stay
in Germany. The table also shows differences in personality for students with a migration intention by
cultural distance to Germany. Contrary to expectations, we find that students who are willing to move to
culturally distant countries significantly have lower levels of extraversion than students that are willing to
move to countries more similar to Germany in terms of culture. They do score higher on agreeableness as
9
For instance, the item “Don’t mind being the center of attention” is a positive keyed item for extraversion. The item “Don’t like
to draw attention to myself” is a negative keyed item for the same trait but it represents the opposite pole that is introversion.
10
We followed the methodology suggested on IPIP website.
11
For age and sex, see, e.g., Faggian et al. (2007), Coniglio and Prota (2008), and Venhorst et al. (2010). For marriage and risk
attitude, see, e.g., Jaeger et al. (2010) and Bauernschuster et al. (2014). For immigrant background and study-related characteristics
such as GPA and study field, see e.g. De Grip et al. (2010). In our cultural distance models, we included geographic distance,
difference of GDP level between Germany and intended destination country, and free mobility dummy to neutralize the geographic,
economic, and bureaucratic factors behind migration decision.
12
467 students who do not have a German passport stated their willingness to work abroad after their graduation. However, when
their intended destination country is crosschecked with their nationality, we observe that 36% of them expressed a willingness to
work in their home countries. Inclusion of these students may confound our analysis since we defined cultural distance by taking
Germany as the reference point.
10
expected. In addition, students who are willing to move to culturally distant countries are significantly less
conscientious and less emotionally stable. They seem to be more open but differences between groups are
not significant.
4 EMPIRICAL STRATEGY
We performed two types of regression analyses to test our hypotheses. First, we estimate a probit regression
(equation 2) where our dependent variable is intention to migrate, and report marginal effects. We expect
to find significant s for each personality trait in the directions explained in Section 2. We control for
risk and time preferences, socio-demographic characteristics such as age, sex, relationship status, and
immigrant background and study-related characteristics such as GPA, and level and field of study.
(2)
(3)
In our second model (equation 3), the dependent variable is cultural distance (equation 1) between
Germany’s and each potential destination country, which we estimate using OLS for the sample of students
who report a migration intention. We expect to find significant s for each personality trait with signs in
the direction explained in Section 2. In this model, we control for a set of students’ socio-demographic and
study-related characteristics that can be relevant for cultural distance such as age, sex, and GPA in addition
to risk and time preferences. We additionally control for geographic distance, differences in GDP, and a
dummy to capture free mobility to the destination country, to isolate geographic, economic, and
bureaucratic factors affecting destination choice.
5 RESULTS
5.1 Migration intentions
Estimation results of probit models for intention to migrate are presented in Table A6. Focusing on the
results in Column 6, one standard deviation increase in extraversion (6.5 points) and openness (4.8 points)
are associated with 1.1% and 2.2% increase in the probability of intending to migrate, respectively.
13
These
estimates account for approximately 0.9% and 2.4% of the unconditional probability of intending to migrate
observed in the sample (19%) for one point increase in extraversion and openness, respectively. Conversely,
13
Unstandardized scores of Big 5 personality traits are presented in Table A9.
11
one standard deviation increase in agreeableness (5.1 points) and conscientiousness (5.6 points) are
correlated with 2.5% and 2.2% decrease in the probability of intending to migrate. These account for 2.6%
and 2.1% of unconditional probability for one unit change in these two traits, respectively. Moreover, we
find that one standard deviation increase in emotional stability (6.6 points) is associated with 1.3% decrease
in probability of intending to migrate. One unit change in this trait is related to 1% change in the probability
of intending to migrate over the unconditional probability.
Our estimates for intention to migrate in Table A6 show that one standard deviation increase in risk aversion
is associated with around 5% decrease in likelihood of having migration intention. Unlike risk preferences,
we do not find a robust significant association of time preferences with intention to migrate. Although
inclusion of risk attitude improves the explanatory power of our model (compare Column 2 to Column 1),
it hardly changes the magnitude of our estimates for personality traits except extraversion. It is in line with
Becker et al. (2012) showing that risk preferences and personality traits are complementary in explaining
individuals’ labor market success, health status, and life satisfaction.
Inclusion of demographic and study-related characteristics in our model for intention to migrate do not
significantly change the marginal effects estimated for personality traits and risk aversion (compare Column
4 and 6 to Column 2). Younger students and students with immigrant background in our sample are more
likely to have migration intentions while students who have a stable relationship are less likely to consider
starting a career abroad. Female students consider starting a career abroad more than male students. This
finding coincides with recent trends in international migration flows that more skilled women have been
internationally mobile for career purposes (Docquier et al., 2009). We unexpectedly find that students with
a higher GPA are less likely to have migration intentions. PhD students tend to have migration intentions
more than undergraduate students while the opposite is observed for master’s students. Migration intentions
tend to vary by field of study as well. For example, compared to economics students, students studying in
mathematics and engineering fields are less likely to have migration intentions. Students in STEM fields
might be more willing to stay in their home country after their graduation as they may perceive labor market
conditions in Germany more favorable considering skill shortages in the German labor market (Bellmann
and Hübler, 2014).
5.2 Cultural distance
Results of OLS estimation for our cultural distance model on the sample of students who express a
migration intention are presented in Table A7. Column 1 shows that one standard deviation increase in
openness (4.9 points for students who have a migration intention) is associated with 0.045 units increase in
12
the cultural distance that German students are willing to migrate. It accounts for 0.9% of unconditional
mean of cultural distance (0.982 units). Unlike openness, conscientiousness is negatively associated with
cultural distance that German students intend to migrate. However, it is not significant when we include
our controls. Contrary to our hypothesis, we find that extraversion is negatively associated with cultural
distance, and propose an explanation for this in Section 5.3 where we investigate migration to German-,
English- and other-speaking countries as an alternative measure to cultural distance.
As Table A7 further shows, increase in risk aversion is negatively associated with cultural distance that
German students are willing to migrate but coefficients are not significant at any standard significance level.
Similarly, we do not find a robust significant association of cultural distance that students are willing to
migrate with their time preferences, age, gender, and GPA. Inclusion of country-specific characteristics
(Column 5) significantly improves the explanatory power of our model but does not alter the correlations
we find for extraversion and openness.
5.3 Language distance
German students in our sample who intend to start a career abroad mostly prefer English or German
speaking countries. These countries to a certain extent share common cultural characteristics as reflected
by pairwise correlations between Hofstede cultural distance index and language dummies in Table A5.
Hence, we use country-language groups as an imperfect substitute for Hofstede cultural distance index to
check the robustness of our main finding. We defined a categorical variable taking 0 for German-speaking
countries, 1 for English-speaking countries, and 2 for other countries and replicated our analysis for cultural
distance. Results of multinomial logit estimation are presented in Table A8.
We find that extravert students are significantly less likely to prefer non-German/non-English-speaking
countries as potential migration destinations. One standard deviation increase in extraversion is related to
2.4% decline in likelihood of having intention to migrate to a non-German/non-English-speaking country
after controlling for risk and time preferences, demographic and study-related characteristics, and country-
specific characteristics. These estimates provide further insights that we do not capture in our main analysis.
Students scoring higher on agreeableness are more likely to prefer non-German/non-English-speaking
countries as one standard deviation increase in agreeableness is associated with 2% increase in having a
preference over non-German/non-English-speaking countries. Furthermore, students scoring higher in
conscientiousness are more likely to prefer German-speaking countries as migration destinations. One
standard deviation increase in conscientiousness is associated with 1.1% increase in likelihood of
considering a German-speaking country as a potential destination.
13
6 CONCLUSION
In this paper, we investigate whether personality traits are related to individuals’ international migration
intentions and preferences over alternative destination countries with different cultural background. We use
the Fachkraft survey with information on German university students’ migration intentions after they
graduate, their preferred destination country, and their Big Five personality traits. The results confirm our
hypotheses that more open and extravert students are more likely to consider moving abroad while more
conscientious and agreeable students are less inclined to migrate. We find that more emotionally stable
students are less likely to have migration intentions. This suggests that emotionally stable individuals are
more satisfied with their current location and community, making them less likely to develop migration
intentions. Such an interpretation is in line with findings from Jokela et al. (2008), Silventoinen et al. (2008),
and Van Den Berg and Feij (1993).
With respect to cultural distance, we find that openness positively and extraversion negatively relate to the
willingness to move to countries that are culturally more remote. This holds even when we control for risk
attitude, time preference, personal characteristics, geographic distance and economic differences between
countries. This suggests an independent relation between personality and cultural distance of migration.
Although the correlation with respect to openness is as expected, this does not hold for extraversion. Using
language distance as an alternative to cultural distance, we show that extraverts are more likely to consider
countries where German or English are official languages. It explains why we observe a negative
association of cultural distance with extraversion in our main analyses. We also find that more agreeable
students are more likely to consider non-German/non-English-speaking countries as potential destinations
when we use language as a cultural distance indicator. Although there is a consensus in migration
psychology literature on that extraverts are more likely to migrate, evidence on their location choice is not
straightforward. Jokela et al. (2008) find that highly sociable (i.e., extravert) individuals are more likely to
migrate longer distances and to prefer urban areas while Jokela (2009) finds that higher extraversion
predicts higher migration flows within but not between states in the US. Ayhan et al. (2017) found a
negative relation between extraversion and propensity to migrate from rural areas to cities and explain this
finding by social individuals’ feeling more attached to their own communities. We think that in our case,
extraverts perceive their utility being lower in linguistically distant countries where they may not easily
involve in social interactions. Hence, they prefer countries where they can easily overcome language
barrier.
Our results suggest a positive self-selection of immigrants in terms of openness as individuals who are more
open to new experiences may integrate into their host countries more easily and faster. However,
14
immigrants are negatively self-selected in terms of conscientiousness which may indicate a slower
economic integration process particularly in the job market considering conscientiousness is often
associated with higher job performance (Almlund et al., 2011). Furthermore, we find that immigrants may
sort themselves into countries where they can easily integrate as indicated by our findings for extravert and
agreeable individuals’ preferences over linguistic characteristics of countries.
15
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19
APPENDIX
Table A1: Dependent and independent variables
Dependent variables
Intention to migrate
Country where a student intends to start a career after finishing her studies
Binary variable: 0 if Germany, 1 if abroad
Cultural distance
Cultural difference between Germany and a student’s preferred destination country to
work
Continuous variable measured by a composite index constructed through four
Hofstede national culture dimensions based on the formula of Kogut and Singh
(1988)
Language distance
Country where a student intends to start a career after finishing her studies
Categorical variable: 0 if a student intends to move one of the German- speaking
countries (Austria, Belgium, Luxembourg, Switzerland), 1 if a student intends to
move one of the English-speaking countries (Australia, Canada, Ireland, New
Zealand, UK, USA), 2 otherwise
Key independent variables
Big Five personality traitsa
Measured by 50-item IPIP inventory (all are standardized continuous variables)
Extraversion
Having an energetic approach towards social and material world
Agreeableness
Having a prosocial and communal orientation towards others with antagonism
Conscientiousness
Having a socially prescribed impulse control that facilitates task- and goal-directed
behavior
Emotional stability
Contrasting with neuroticism associated with proneness to feel anxious, nervous,
sad, and tense
Openness
The breadth, depth, originality, and complexity of an individual’s mental and
experiential life
Control variables
Risk aversion
Standardized continuous variable (increase in the variable indicates increase in risk
aversion.)
Impatience
Standardized continuous variable (increase in the variable indicates increase in
impatience.)
Age
Age of students in years, continuous variable
Female
Binary variable: 0 if male (base group), 1 if female
Having a stable relation
Relationship status of the student (base group: no relationship)
Binary variable: 1 if student has a stable relationship or is married, 0 otherwise
Immigrant background
Whether a student has at least one foreign-born parent
Binary variable: 0 if both parents have German passport, 1 otherwise
GPA
Indicator of academic success, measured by grade point average at the time of the
survey
Level of study
Categorical variable for the degree followed: 0 if Bachelor’s (base group), 1 if
Master’s, 2 if PhD
Study field
Categorical variable for study fields:
Education, computer sciences, engineering sciences, art & music, mathematics,
media & communication, medical sciences, natural sciences, psychology, law,
religion, social sciences & humanities, sports, language & culture, economics (base
group)
Geographic distanceb
Distance between central points of Germany and a student’s preferred destination
country measured in 1,000 kilometers
Diff GDP levelc
Log difference between a student’s preferred destination country’s and Germany’s
average GDP per capita in the period 2010-2014
Free mobility
Dummy variable taking 1 if a student intends to move one of the EU member countries,
0 otherwise
Notes: a Definitions of traits are taken from John et al. (2008)
b Calculated using geodist command in Stata which uses a mathematical model of the earth to calculate the length of the
shortest curve between two points.
c Data is retrieved from World Bank’s World Development Indicators (2016, September 08).
20
Table A2: Summary statistics
Variable
N
Mean
Std. Dev.
Min
Max
Dependent variables
Intention to migrate
8,572
0.191
0.393
0
1
Cultural distance (Hofstede)
1,638
0.982
0.904
0.033
4.589
Language distancea
1,638
1.306
0.649
0
2
German-speaking country
171
0.104
English-speaking country
795
0.485
Other countries
672
0.410
Big 5 personality traits
Extraversion
8,572
0
1
-2.701
2.647
Agreeableness
8,572
0
1
-4.924
1.940
Conscientiousness
8,572
0
1
-3.355
2.738
Emotional stability
8,572
0
1
-2.524
2.780
Openness
8,572
0
1
-3.874
2.562
Controls
Risk aversion
8,572
0
1
-2.513
3.330
Impatience
8,572
0
1
-2.434
3.165
Age
8,572
22.24
2.862
17
30
Female
8,572
0.599
0.490
0
1
Having a stable relation
8,572
0.559
0.497
0
1
Immigrant background
8,572
0.183
0.387
0
1
GPA
8,572
3.704
1.462
1
8
Level of studya
8,477
0.310
0.475
0
2
Bachelor’s
5,900
0.696
Master’s
2,526
0.298
PhD
51
0.006
Study fielda
8,572
8.972
4.902
1
15
Education
367
0.043
Computer sciences
492
0.057
Engineering sciences
1,289
0.150
Art & Music
228
0.027
Mathematics
183
0.021
Media & Communication
486
0.057
Medical sciences
348
0.041
Natural sciences
807
0.094
Psychology
260
0.030
Law
384
0.045
Religion
54
0.006
Social sciences & Humanities
969
0.113
Sports
99
0.012
Language & Culture
727
0.085
Economics
1,879
0.219
Geographic distance
1,638
4.763
4.522
0.333
18.31
Diff GDP level
1,638
-0.170
0.719
-3.392
0.859
Free mobility
1,638
0.345
0.476
0
1
Source: Authors’ tabulation
Notes: a Mean values of sub-categories of language distance, level of study, and study field represent percentage
distribution of sub-categories in the sample.
21
Table A3: Descriptive statistics of students’ preferred destination countries
Country
# Students
Cultural distance
Geographic distance
Diff GDP
Argentina
13
0.549
12.035
-1.296
Australia
93
0.328
14.263
0.199
Austria
40
0.489
0.460
0.090
Belgium
15
0.968
0.415
0.020
Brazil
13
1.209
9.119
-1.328
Bulgaria
1
1.851
1.497
-1.828
Canada
75
0.339
6.254
0.114
Chile
6
2.393
12.468
-1.148
China
47
2.527
7.540
-2.109
Colombia
9
1.858
9.249
-1.857
Costa Rica
1
2.694
9.452
-1.631
Croatia
3
1.801
0.796
-1.161
Czech Republic
3
0.380
0.389
-0.780
Denmark
42
2.642
0.434
0.284
Ecuador
3
2.435
10.000
-2.146
Estonia
1
0.923
1.302
-0.980
Finland
18
1.109
1.829
0.058
France
54
1.133
0.738
-0.056
Greece
2
1.781
1.828
-0.603
Guatemala
1
4.589
9.373
-2.710
Hungary
2
0.589
0.801
-1.181
India
8
1.423
6.559
-3.344
Indonesia
1
2.545
11.042
-2.543
Iran
5
0.935
4.086
-1.968
Ireland
43
0.427
1.272
0.135
Israel
11
0.683
2.980
-0.310
Italy
29
0.207
0.936
-0.223
Japan
35
1.264
9.076
0.003
Latvia
1
2.251
1.147
-1.227
Luxembourg
10
0.218
0.333
0.859
Malaysia
2
3.755
10.738
-1.494
Malta
1
0.926
1.720
-0.779
Mexico
18
1.820
9.402
-1.554
Morocco
5
0.949
2.846
-2.668
Netherlands
51
1.971
0.366
0.145
New Zealand
25
0.305
18.309
-0.221
Norway
34
2.389
1.994
0.708
Panama
1
3.728
9.389
-1.544
Peru
6
2.131
10.386
-2.065
Philippines
2
2.543
10.263
-2.933
Poland
7
0.942
0.618
-1.179
Portugal
1
2.579
1.916
-0.693
Romania
3
2.831
1.234
-1.615
Russia
8
3.106
4.625
-1.350
Singapore
15
3.369
10.137
0.123
Slovenia
3
3.080
0.654
-0.632
South Africa
11
0.223
9.092
-1.753
South Korea
11
2.001
8.546
-0.625
Spain
66
0.946
1.598
-0.376
Sweden
72
3.106
1.428
0.193
Switzerland
106
0.033
0.483
0.545
Thailand
8
2.059
8.725
-2.087
Turkey
31
1.357
2.363
-1.391
United Kingdom
226
0.597
0.891
-0.105
United States of America
333
0.423
7.862
0.128
Venezuela
2
2.464
8.484
-1.130
Vietnam
5
2.563
9.100
-3.392
Source: Authors’ tabulation
Notes: First column represents number of students who prefer moving to the countries listed (57 countries). The other columns
show the cultural distance, geographic distance, and difference in GDP level between Germany and the countries listed,
respectively.
22
Table A4: Mean values of personality traits across students
Intention to migrate
Cultural distance
No
Yes
Diff
Low
High
Diff
Big 5 personality traits
Extraversion
-0.014
0.061
-0.075***
0.028
-0.045
0.073*
(0.028)
(0.051)
Agreeableness
0.026
-0.110
0.136***
-0.027
0.043
-0.070*
(0.027)
(0.051)
Conscientiousness
0.025
-0.107
0.132***
0.061
-0.097
0.158***
(0.027)
(0.051)
Emotional stability
0.017
-0.070
0.087***
0.028
-0.045
0.073*
(0.028)
(0.051)
Openness
-0.024
0.102
-0.126***
-0.001
0.001
-0.002
(0.027)
(0.051)
Observations
6,934
1,638
1,009
629
Source: Authors’ tabulation
Notes: Standardized scores of personality traits are reported. High (low) cultural distance refers to being above (below)
median value observed in the sample. Differences at means are significant at *** p<0.01, ** p<0.05, * p<0.1.
23
Table A5: Pairwise correlations of characteristics of students’ preferred destination countries
Cultural
distance
Geographic
distance
Diff GDP
level
Free
mobility
English
speaking
country
German
speaking
country
Cultural distance (Hofstede)
1.0000
Geographic distance
0.2317
1.0000
0.0829
Diff GDP level
-0.3930*
-0.3814*
1.0000
0.0025
0.0034
Free mobility
-0.1559
-0.7903*
0.4564*
1.0000
0.2468
0.0000
0.0004
English-speaking country
-0.3994*
0.2147
0.3406*
-0.2925*
1.0000
0.0021
0.1087
0.0095
0.0272
German-speaking country
-0.3138*
-0.2916*
0.3618*
0.3221*
-0.0942
1.00000
0.0175
0.0278
0.0057
0.0145
0.4857
Source: Authors’ tabulation
Notes: Number of observations is 57. Significance levels are reported under each Pearson correlation coefficient. * p<0.05.
English-speaking country is a dummy that takes 1 if a student intends to move to one of the English-speaking countries
(Australia, Canada, Ireland, New Zealand, UK, USA), 0 otherwise. German-speaking country is a dummy that takes 1 if a
student intends to move to one of the German-speaking countries (Austria, Belgium, Luxembourg, Switzerland), 0 otherwise.
24
Table A6: Probit estimates for intention to migrate
Variables
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Big Five personality traits
Extraversion
0.018***
0.009*
0.010**
0.011**
(0.005)
(0.005)
(0.005)
(0.005)
Agreeableness
-0.027***
-0.024***
-0.027***
-0.025***
(0.004)
(0.004)
(0.005)
(0.005)
Conscientiousness
-0.019***
-0.022***
-0.021***
-0.022***
(0.005)
(0.005)
(0.005)
(0.005)
Emotional stability
-0.014***
-0.017***
-0.014***
-0.013***
(0.004)
(0.004)
(0.005)
(0.005)
Openness
0.026***
0.024***
0.025***
0.022***
(0.005)
(0.004)
(0.004)
(0.005)
Risk aversion
-0.050***
-0.051***
-0.046***
-0.048***
-0.047***
-0.049***
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
(0.004)
Impatience
-0.004
0.008*
-0.005
0.006
-0.003
0.008*
(0.005)
(0.004)
(0.005)
(0.004)
(0.005)
(0.004)
Age
-0.007***
-0.007***
-0.006***
-0.006***
(0.001)
(0.001)
(0.002)
(0.002)
Female
0.031***
0.019**
0.022**
0.010
(0.009)
(0.009)
(0.010)
(0.009)
Having a stable relation
-0.100***
-0.102***
-0.098***
-0.100***
(0.009)
(0.009)
(0.009)
(0.009)
Immigrant background
0.042***
0.040***
0.038***
0.037***
(0.011)
(0.011)
(0.011)
(0.011)
GPA
-0.010***
-0.010***
(0.003)
(0.003)
Level of study (base: Bachelor’s)
Master’s
-0.040***
-0.041***
(0.010)
(0.010)
PhD
0.127*
0.147**
(0.067)
(0.068)
Study field (base: Economics)
Education
-0.107***
-0.107***
(0.019)
(0.019)
Computer sciences
-0.003
0.000
(0.020)
(0.020)
Engineering sciences
-0.040***
-0.037***
(0.014)
(0.014)
Art & Music
0.009
0.034
(0.028)
(0.029)
Mathematics
-0.102***
-0.099***
(0.025)
(0.025)
Media & Communication
-0.019
-0.009
(0.020)
(0.020)
Medical sciences
0.017
0.022
(0.025)
(0.025)
Natural sciences
-0.010
-0.006
(0.017)
(0.017)
Psychology
-0.036
-0.033
(0.025)
(0.025)
Law
-0.041*
-0.033
(0.022)
(0.023)
Religion
-0.084*
-0.089*
(0.050)
(0.047)
Social sciences & Humanities
-0.042***
-0.034**
(0.015)
(0.015)
Sports
-0.076**
-0.075**
(0.034)
(0.033)
Language & Culture
0.059***
0.073***
(0.019)
(0.019)
Observations
8,572
8,572
8,572
8,572
8,572
8,477
8,477
Pseudo R-squared
0.0125
0.0284
0.0174
0.0521
0.0408
0.0679
0.0577
Log pseudolikelihood
-4129.17
-4062.46
-4108.80
-3963.44
-4010.96
-3855.99
-3898.13
Source: Authors’ estimation
Notes: Dependent variable is intention to migrate, binary variable taking 0 if a student intends to start her career in Germany and 1 if abroad. Sample
size in columns 6 and 7 is smaller due to missing entries in one of our controls, level of study. Marginal effects from probit estimations are presented in
columns. Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
25
Table A7: OLS estimates for cultural distance
Variables
(1)
(2)
(3)
(4)
(5)
Big Five personality traits
Extraversion
-0.051**
-0.055**
-0.052**
-0.053**
(0.024)
(0.025)
(0.025)
(0.021)
Agreeableness
0.011
0.012
0.015
0.001
(0.025)
(0.025)
(0.025)
(0.022)
Conscientiousness
-0.047**
-0.041
-0.035
-0.007
(0.022)
(0.026)
(0.026)
(0.023)
Emotional stability
0.002
0.002
-0.005
0.009
(0.023)
(0.023)
(0.024)
(0.021)
Openness
0.045*
0.045*
0.046*
0.044**
(0.024)
(0.024)
(0.024)
(0.020)
Risk aversion
-0.021
-0.014
-0.018
-0.013
(0.023)
(0.022)
(0.023)
(0.020)
Impatience
0.014
0.033
0.011
0.003
(0.027)
(0.022)
(0.026)
(0.023)
Age
0.023***
0.009
(0.008)
(0.007)
Female
-0.026
-0.032
(0.052)
(0.045)
GPA
0.015
0.015
(0.016)
(0.014)
Geographic distance
0.012***
(0.003)
Diff GDP level
-0.569***
(0.028)
Free mobility
0.840***
(0.048)
Constant
0.982***
0.982***
0.982***
0.433**
0.303*
(0.022)
(0.022)
(0.022)
(0.194)
(0.162)
Observations
1,638
1,638
1,638
1,638
1,638
R-squared
0.006
0.007
0.001
0.013
0.277
Source: Authors’ tabulation
Notes: Dependent variable is cultural distance measured by the difference of Germany from the
most preferred destination country a student is willing to migrate in a composite index
constructed through four Hofstede national culture dimensions based on the formula of Kogut
and Singh (1988). Coefficients from OLS estimations are presented in columns. Robust standard
errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
26
Table A8: Multinomial logit estimates for language distance
German-speaking country
English-speaking country
Other countries
Variables
(1)
(2)
(3)
(4)
(5)
(6)
Big Five personality traits
Extraversion
0.014*
0.003
0.013
0.021**
-0.027**
-0.024**
(0.008)
(0.005)
(0.013)
(0.011)
(0.013)
(0.011)
Agreeableness
-0.019**
-0.011**
-0.010
-0.009
0.029**
0.020*
(0.008)
(0.005)
(0.013)
(0.010)
(0.013)
(0.011)
Conscientiousness
0.021***
0.011*
0.024*
-0.005
-0.045***
-0.006
(0.008)
(0.006)
(0.013)
(0.012)
(0.013)
(0.013)
Emotional stability
-0.004
0.001
0.008
0.004
-0.004
-0.005
(0.008)
(0.005)
(0.013)
(0.010)
(0.013)
(0.011)
Openness
-0.000
-0.006
-0.014
0.001
0.014
0.004
(0.008)
(0.005)
(0.013)
(0.010)
(0.013)
(0.011)
Risk aversion
0.001
0.004
-0.005
(0.005)
(0.009)
(0.010)
Impatience
-0.003
-0.007
0.009
(0.006)
(0.011)
(0.012)
Age
-0.001
-0.006*
0.007*
(0.002)
(0.003)
(0.004)
Female
-0.004
0.019
-0.015
(0.010)
(0.022)
(0.024)
GPA
-0.002
-0.010
0.012
(0.003)
(0.006)
(0.007)
Geographic distance
-0.158***
0.117***
0.041***
(0.015)
(0.008)
(0.008)
Diff GDP level
0.200***
0.225***
-0.425***
(0.008)
(0.011)
(0.012)
Observations
1,638
1,638
1,638
1,638
1,638
1,638
Pseudo R-squared
0.0088
0.4353
0.0088
0.4353
0.0088
0.4353
Log pseudolikelihood
-1546.07
-880.79
-1546.07
-880.79
-1546.07
-880.79
Source: Authors’ tabulation
Notes: Dependent variable is language distance, a categorical variable taking 0 if a student reports a German-speaking
country as a preferred destination, 1 if an English-speaking country, and 2 otherwise. Marginal effects from multinomial
logit estimation are presented in columns. Free mobility dummy is excluded in specifications with controls to properly
estimate standard errors since all German-speaking countries have a free mobility agreement with Germany and none of
English-speaking countries have such an agreement. Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, *
p<0.1.
27
Table A9: Descriptive statistics of unstandardized scores of Big 5 personality traits
Big 5 personality traits
N
Mean
Std. Dev.
Min
Max
Full sample
Extraversion
8,572
32.677
6.545
15
50
Agreeableness
8,572
40.109
5.099
15
50
Conscientiousness
8,572
34.720
5.580
16
50
Emotional stability
8,572
31.655
6.599
15
50
Openness
8,572
37.660
4.817
19
50
Sample of students intending to migrate to countries for which Hofstede index is available
Extraversion
1,638
33.075
6.580
15
50
Agreeableness
1,638
39.549
5.297
18
50
Conscientiousness
1,638
34.123
5.897
17
50
Emotional stability
1,638
31.192
6.825
15
50
Openness
1,638
38.150
4.870
21
50
Source: Authors’ tabulation
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