DP2019-15

Cultural Factors and Study
Destinations of International
Students*
Hao WEI
Ran YUAN
Laixun ZHAO

July 17, 2019

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Cultural Factors and Study Destinations of International Students*

by
Hao Wei*, Ran Yuan* and Laixun Zhao†

Abstract: We examine the role of cultural factors in attracting international
students, using data of 102 countries from 2000 to 2015. Our results show that theexport of cultural products is conducive to the increase of international students, and inparticular, international students choose to study in developing countries whose official
language and religious beliefs are different from their home countries, while they tend
to go to developed countries with a common language. We also examine the featuresof international students in China and Chinese students in other countries. The policy
implication from our study is that “soft power” such as a unique culture, common value
and migration networks is important in attracting foreign students.
Keywords: Cultural factors, international students, cultural goods exports,
migration networks, Chinese students abroad
JEL Classification: F16; I23*: Dept. of International Economics, Beijing Normal University, Beijing, China;†: Corresponding author, Research Institute for Economics & Business, Kobe University,
Kobe 657-8501, Japan, E-mail: zhao@rieb.kobe-u.ac.jp

01. Introduction
Studying abroad is a common and universal phenomenon in the globalized economy.
According to UNESCO statistics, the number of international students rose from 2
million in 2000 to 4.6 million in 2015. 1 Together with the increasing scale, thetrajectory of international students has also changed in the last few decades. While ithas been historically characterized as going in one direction from developing countries
to developed countries or from secondary developed countries to leading developed
countries since the 1960s, the new pattern can be summarized as two-way circulation,i.e., mutual mobility between developing and developed countries, from the late 20th
century (OECD, 2008). It is thus interesting and important to investigate themotivations for such student mobility and the causes of the changes.
Cultural proximity can be a critical dimension, but is hard to measurecomprehensively. International students may experience a great deal of unfamiliarity inalmost every aspect of their lives in the host countries because of the differences inlanguage, culture, religion and educational systems. However, there is no existing
unified framework that explains the role of cultural factors in shaping international
student mobility.
In this paper, we attempt to accomplish this task, using data of 102 countries andregions for 2000-2015. Our main results indicate that the export of cultural products is
conducive to the increase of international students, and the immigrant stock is beneficial
for attracting international students from the source countries. Moreover, we find thatinternational students choose to study in developing countries whose official language
and religious beliefs are different from their home countries, while they tend to go todeveloped countries with a common language. We also examine the features ofinternational students in China and Chinese students in other countries. The policy
implication from our study is that “soft power” such as a unique culture, common value
and migration networks is important in attracting foreign students.

1

UNESCO Institute for Statistics, http://uis.unesco.org/
1Our study complements the existing literature in the following aspects: (1) So far
most research chooses one country as the host country, and studies the bilateral student
flow between this country and other countries. In contrast, we perform an overall
analysis from a global perspective with many countries. (2) The existing literature
mainly focuses on developed countries, while we fill the gap by including and focusing
on developing countries, with special attention to China. (3) Most existing literatureuses a single index to measure the cultural factors, by including only one of several
potential factors such as language, religion and other variables, which cannot
comprehensively reflect the implications of the full spectrum of culture. In contrast, thepresent paper measures cultural proximity from several key aspects: including timevariant

andtime-invariant

factors, proposing a uniformed

framework tocomprehensively explore their role of culture in international student mobility, andespecially incorporating the volume of various cultural-goods trade and the number of
immigrants who can transmit foreign culture.
Some existing studies investigate the impacts of language proximity, religious
proximity and migration networks on the destination choice of international students,
but are markedly different from our analysis. For example, Chen and Barnett (2000)
argue that international student exchanges based on linguistic relationships havebecome less significant, while economic and political development plays increasingly
important roles; The “push-pull” framework studies the relationship between student
outflow and the educational, economic and political dimensions in the host country(Mazzarol and Soutar, 2002); Racine, Villeneuve and Thériault (2003) investigate thecase of students in Québec, Canada, finding that language is a decisive factor forinternational students flowing to this region; Park (2009) shows for South Korean
students, academic expectations and the cultural environment are the most importantfactors in the selection of host countries; González, Mesanza and Mariel (2011) findthat academic quality, cost of living, language, distance and other factors cansignificantly influence the number of international students in a country; Kondakci(2011) finds that climate, food preference, language, cultural experience and otherfactors can influence international students’ choices in Turkish public universities; and2

Bouwel and Veugelers (2013) use shared language to measure cultural similarity, andfind it has positive but insignificant impact on student mobility among 31 European
countries. With regard to migration networks, Beine et al. (2014) find that migration
networks, costs and the quality of universities are the most important factors, and Beech(2015) argues that social networks of friendship and kinship are critical determinants
for students deciding to study overseas.
The rest of the paper is organized as follows: Section 2 presents the econometricmodel and explains the data; Section 3 reports our empirical results; Section 4 conducts
robustness checks; and Section 5 concludes with some policy recommendations.
2. Some basic facts of international student mobility
2.1 International students in higher education around the world
The demand for a knowledge-based economy and highly skilled human resources
drives the rapid expansion of higher education. The number of students engaged in
tertiary education worldwide has exploded within a generation, rising from 97 million
in 2000 to 231 million in 2015 (UNESCO, 2019).2 Since 2000, the gross enrollment
ratio has risen by 29 percent in upper middle income countries, from 17% to 46%,
compared to 25 percent in Eastern and Southeastern Asia, and in Latin America and the
Caribbean. At the same time, the number of students who choose to study abroad has
increased from 1.3 million in 1990 to 4.7 million in 2015, demonstrating a newgeneration of young people eager to learn and expand their horizons. And this number
is expected to further increase to as many as 8 million by 2025 (OECD, 2017).3
Table 1 shows inbound and outbound students of the top 10 countries in 2015. In
terms of geographical distribution, we find that, (a) English-speaking countries areamong the biggest hosts of international students, with the United States (U.S.)
enrolling about one-fifth of the world total, more than double that in the United
Kingdom (U.K.), which is the No. 2 host. Specifically, about 37.67 percent of all

2

UNESCO (2018). Global Education Monitoring Report 2019, https://unesdoc.unesco.org/ark:/48223/pf000026
5866/PDF/265866eng.pdf.multi
3 OECD (2017). Education at a Glance 2017, https://www.oecd-ilibrary.org/education/education-at-a-glance-2017_eag-2017-en3international students enroll in four English-speaking countries (the U.S., the U.K.,
Australia and Canada). (b) Non-English speaking countries such as France andGermany are also large hosts, accounting for 5.0 and 4.7 percent respectively. (c) China
has entered the market for students more recently, quickly becoming the third largest
host and the largest developing destination for international students.
On the other hand, in terms of outbound students, we find that: (a) China is thelargest origin country of international students, contributing about 17.1% of the world
total, more than three times than those from India which is the second largest origin
country. (b) Asian students studying in developed countries make up a large share. In
2015, the top three largest origin countries are in Asia, namely China, India and theRepublic of Korea. These countries together account for about 6.66 percent of alloutbound students. (c) Some European countries, such as Germany and France are also
main senders of international students, accounting for 2.4 and 1.8 percent respectively.
Table 1 Inbound and outbound students of the top 10 countries (2015)
Destination

Inbound

OriginOutboundUnited States

907251

China

819524

United Kingdom

430687

India

256636

China

397635

Republic of Korea

107762Australia

294438Germany117104

France239409Nigeria

92476

Germany228756

France86690

Russian Federation

226431

Saudi Arabia

86242

Canada171603

Kazakhstan

78260

Japan

131980

United States

70553

Malaysia

111443

Viet Nam

68046% of Total

65.57%% of Total

37.24%

Source: The data are mainly derived from UIS statistics by the authors, and the inbound data of international students
in China are from China Ministry of Education.

2.2 International students in higher education in China
Much of the mobility of international students has been one direction in recent
history: from the South to the North. However, China has somewhat changed thishistory. In 2000, only 0.05 million international students studied in China, which merely
accounts for about 3.09 percent of the world total. By the end of 2015, the share has
4increased to 8.63 percent and more than 0.397 million in total. Among them, 184,799
came to receive academic education, accounting for 46.47 percent of the total.
Moreover, postgraduate students and doctoral students account for about 21.2% and7.8% respectively. Columns (1) and (2) in Table 2 list the inbound students from the
top 10 countries. We find that, (a) although the source covers 203 countries and regions,the top 10 origin countries contribute more than half of all international students in 2015.
Most of the top origin countries are neighbors or countries with close economic
exchanges, such as the U.S. and France. (b) The Republic of Korea remains the mostimportant source. In 2015, it accounts for 16.77% of the total, which is more than threetimes the share of the U.S. (c) The top 10 source countries remain unchanged, but thecountries along the “belt and road initiative” (BRI) become the new growth point forinternational students in China. In 2015, six of the top 10 origin countries are thepartners of BRI, namely Thailand, India, Russia, Pakistan, Kazakhstan and Indonesia.
Although China has become the third largest destination country, its international
education level is relatively low. According to Education at a Glance 2015 publishedby the OECD,4 the proportion of international students at school is respectively 17.97
and 3.87 percent in Australia and the U.S., compared to only 0.46 percent in China.
These figures indicate a huge room for China to attract more international students.
On the other hand, for outbound students from China, the total number stands at
0.813 million in 2015. By the end of 2015, Chinese students account for more than 30
percent of the total international students in the U.S. and Canada, and about 27.3 percent
in Australia. The percentage reaches as high as 55.9 and 62 percent, respectively inSouth Korea and Japan. In some European countries, China is also the most importantsource of their international students, accounting for about 10 percent, such as in the
U.K., Russia and Sweden. Columns (3) and (4) in Table 2 list outbound students to the
top 10 countries from China. We find that, (a) Chinese students are highly concentrated
in the top 10 destinations that have attracted more than four-fifths of China’s outboundstudents in 2015. (b) English-speaking countries are their most popular destinations,4OECD (2015), Education at a Glance 2015, http://www.oecd.org/education/education-at-a-glance-2015.htm
5perhaps for their high-quality education and better opportunities with the English
language after graduation. (c) The U.S. is the top destination for Chinese students. In
2015, it receives more than twice as many Chinese students than the other three big
English-speaking countries, the U.K., Canada and Australia. (d) The deficit between
the outbound and inbound students has increasingly become a challenge. The net
outflow of Chinese students has increased from 0.102 million in 2000 to 0.422 million
in 2015, which has more than quadrupled in the past 15 years.
Table 2 Inbound and outbound students in the top 10 countries (China, 2015)
OriginInbound

Destination

OutboundRepublic of Korea

66672

United States

291063

United States

21978

Australia

97387

Thailand19976

United Kingdom

91518

India

16694

Japan

79175

Russia16197

Canada54660

Pakistan

15654

Republic of Korea

34513

Japan

14085

France25297

Kazakhstan

13198

Germany

23616

Indonesia

12694

New Zealand

15009

France

10436

Italy

12581

% of Total

52.20%% of Total

88.44%

Source: The outbound data are derived from UIS statistics by the authors, and the inbound data are from China
Ministry of Education.

3. Econometric model and data
3.1. Related definitions(1) International Students. According to the definition of UNESCO Institute for
Statistics (UIS), international students are students who have crossed a national or
territorial border for the purpose of education and are now enrolled outside their countryof origin. At the 29th General Conference in November, 1997, UNESCO endorsed the“International Standard Classification of Education (ISCED)”. It divides education into
six levels: Level 0--Pre-primary education, Level 1--primary education or the first stageof basic education, Level 2--lower secondary or the second stage of basic education,
Level 3–(upper) secondary education, Level 4–post-secondary non-tertiary education,
Level 5–first stage of tertiary education (not leading directly to an advanced research
6qualification) and Level 6–second stage of tertiary education (leading to an advanced
research qualification). The higher education discussed in this paper includes Levels 5
and 6, specifically, short-cycle tertiary education, Bachelor’s or equivalent level,
Master’s or equivalent level, Doctoral or equivalent level.5(2) Cultural Products and Trade in Cultural Goods. According to UNESCO,
cultural goods are defined as consumer goods that convey ideas, symbols, and ways oflife.6 In 2005, UNESCO updated the classification and statistical description of tradein cultural products, and divided cultural goods into core products and related products.
In this paper, we use the trade data of core cultural products only. It includes cultural
heritage (antiques), printed matter (books, newspapers and periodicals, etc.), music andthe performing arts (recording media), visual arts (painting, etc.) and audio andaudiovisual media (photography, cinema and news media), given data availability. At
the three- and four-digit SITC Rev.3 level, cultural goods are composed of 8961, 8962,
8963, 8964, 8965, 8966, 8921, 8922, 8924, 8986, 8987, 8826, 883, and 89431.
3.2. Econometric model and data
Based on the gravity model, we initially choose independent variables considering
three aspects: cultural factors, economic factors and educational factors,7 and set up
the following econometric model:

ln mobstuijt   0  X ijt  CPijt  i   j  t  ijt

(1)

In (1), mobstuijt indicates the number of international students flowing fromcountry i to country j; Xijt is a set of controls, including five variables: (1) the ratio of
GDP per capita of the host country to the home country (gdpperratioijt); (2) thepurchasing power parity of the host country (pppijt); (3) the share of the host country’s
higher education expenditure in its GDP (expegdpjt); (4) the student-teacher ratio of thehost country’s higher education (stutea); (5) the population weighted geographic

5

For more information, see: http://www.unesco.org/education/information/nfsunesco/doc/isced_1997.htm.
UNESCO Institute for Statistics (2005): International flows of selected cultural goods and services, 1994
-2003. http://www.uis.unesco.org/Library/Documents/culture05_en.pdf.
7 We exclude political factors that have been extensively studied in the literature (see for instance Caruso and Wit,
2015, who find that the crime rate in the destination countries is insignificant in attracting international students).
6

7

distance (distanceijt); CPijt is the set of cultural proximity factors; λi and λj are thecountry fixed effects, λt represents the year fixed effects, and εijt is the error term.
In addition, we propose to add the following four indicators. The first two
measures the time-variant cultural factors, while the latter two are time-invariant:(i) Exports of cultural goods (lncultradeijt). We use the export value of cultural
products from country i to country j in year t to represent the cultural output of thestudents’ destination country. Cultural products contain the customs and traditions,values and beliefs of a country. The trade in cultural products is associated with theexchange of culture and information between countries (see Disdier et al., 2010).
(ii) International migration network (lnmigrantijt). We use the total stock ofmigrants from country i to country j in year t to measure the scale of internationalmigration networks. Immigrants bring the characteristic traditions of their religion andculture with them, and promote cultural exchanges between countries (see Scheffler,2007). They can also play a major role for their followers as a bridge to inclusion in a
new cultural environment (Fone and Alba, 2008). It is more likely that international
students will choose a country with more national migrants as their destination. Whilethe data of bilateral migrant stocks is on the bases of every five-years, we supplement
the gap between two intervals by using the data in the initial year.
(iii) Common official language (languageij). Following Helpman, Melitz and
Rubinstein (2008) and Beine et al. (2014), we use a dummy variable to measurelinguistic proximity. Language is the most important part of a culture. In the broadest
sense, language is also the symbolic representation of a people, since it comprises their
historical and cultural backgrounds (Jiang, 2000; Brown, 1994). Students studying incountries with similar languages are easier to communicate, and these similarities easethe cultural shock and cultural conflicts in the host country.
(iv) Common religion (religionij). A dummy variable is used to measure religious
proximity. International students prefer to migrate to a destination country where their
old religion can be practiced in a friendlier environment, and they face incentives toswitch to a less costly religion (see Chiswick, 2014).
With the above incorporated, then Eq. (1) can be modified as:
8ln mobstuijt   0  X ijt  1lncultradeijt   2 ln migrantijt  3languageij   4 religionij  i   j  t   ijt(2)

3.3. Data specification
The data comes mainly from the UN Comtrade (the United Nations Commodity
Trade Database), UIS (UNESCO Data Research Center), OECD (Organization for
Economic Co-operation and Development), IIE (Institute of International Education),
CEPII (the French Centre d'Etudes Prospectives et d'Informations Internationales)
database, the UN immigration database and other official websites. The bilateral dataof international students in China is derived from China’s Ministry of Education. The
data on cultural goods trade are arranged by the 5-digit "Standard International Trade
Classification (Third Edition)" (SITC Rev.3). Table 3 provides a description of thevariables used in our analysis.
It should be noted that, in a model involving multiple countries, it is necessary totake into account the zero value of the dependent variable. That is, international students
from a certain country may prefer to move to some countries but not others, andinternational students in a destination country may have come mainly from some certain
origin countries. If we arbitrarily remove the zero values from our sample, it will resultin a loss of useful information, leading to biased estimation (Eichengreen and Irwin,1998). To resolve this problem, we follow the method in Liu and Qiu (2016), and add
1 to the dependent variable in the benchmark regression and then take logarithm. Thistransformation allows us to keep all observations. Later in the robustness checks, we
also use another log-like transformation to test our results.

Table 3: Descriptive statistics by countries
Full Sample

Developing Destinations

Developed Destinations

Mean

Std. dev.

Mean

Std. dev.

Mean

Std. dev.

gdpperratio

8.946726.1645

4.212917.1500

18.4143

36.4837

lndistance

15.5188

0.822815.6400

0.7292

15.27640.9379ppp

1.19071.26271.35061.51040.87090.2593expegdp

4.5436

1.69504.17111.80995.27251.1325stutea15.8382

6.855818.3974

7.259011.3872

2.4295lncultrade

6.4114

6.63914.7183

6.15159.79766.2734lnmigrant

2.96533.76081.62083.05475.6542

3.59499

language

0.09490.29310.10570.30750.07340.2608religion

0.13980.23250.12680.22840.16580.2384

4. Empirical Analysis
4.1. Student flow in a global perspective
We try to adopt a global perspective, by selecting 102 countries or regions as
destination countries. Our empirical strategy is as follows: first, we analyze the fourmost basic explanatory variables as in Eq. (1) and obtain regression results; then we
gradually add the cultural variables to be tested into the regressions following Eq. (2);and finally, we run regressions on all the variables.
As can be seen from Table 4, in both Model 3 and Model 4, the coefficients ofcultural goods trade are positive and significant at the 1% level. It indicates that, froma global perspective, with the increase of one country's exports of cultural goods, its
ability to attract international students also increases. To some extent, the exports ofcultural products can be perceived as a kind of soft power. For instance, the international
appeal of media culture has become the focal point of soft power in many policy
discussions (Iwabuchi, 2015); Also, many governments encourage the private sector to
produce and export commodified culture (Otmazgin, 2012).
Because cultural products contain customs and traditions, values and beliefs,
international trade in cultural products is equivalent to the exchange of culture andinformation between countries. The more cultural products one country exports, themore attractive and valuable its culture is to other countries. Such exports can also
induce more people to become in favor of the country's culture, values, customs, regime,etc., a good example of which is the so-called “American Dream”.
In Table 4, the coefficients of immigrants (lnmigrant) are positive. Theinterpretation is that the more immigrants one country has, the stronger is its ability to
attract international students. It should be clear that these migrants include all categories
and in particular economic migrants, people coming under family reunification andprevious students settling in the country where they receive education. These migrants

10are likely to provide assistance and information to students from their home countries
and therefore decrease the migration costs. The presence of country nationals at a
destination tends to act as a magnet for international students for several reasons.
Immigrants from the source countries have various connections to their homelands, andimmigration makes them feel closer to each other. The existence of an internationalmigration network has a large positive pushing effect both physically and mentally. A
large number of immigrants in a country indicates the country is open and tolerant, sothat foreign immigrants are able to live peacefully there.

Table 4: Regression results of 102 countries and regions
Model 1
lngdpperppp
expegdp
stuteaModel 2Model 3Model 41.0090***0.18090.6022**0.2012(3.93)(0.65)(2.46)(0.72)

0.00110.00130.00090.0012(0.94)(1.49)(0.94)(1.40)

0.21880.3257**0.3250**0.3447**(1.13)(2.23)(2.00)(2.38)-0.1024**-0.1350**-0.1110**-0.1318**(-2.10)(-2.55)(-2.44)(-2.55)0.5327***lncultrade

0.4111***(4.87)
lnmigrant(2.68)
0.7307***0.3022(3.87)(1.25)-2.2258-4.7869**-8.3274***-6.7079**(-0.95)(-2.00)(-2.90)(-2.27)

Observations

1170

1169

1170

1169

R2

0.25620.35450.32820.3616

Constant

Note: *, **, *** indicate that the variable is significant at the 10%, 5% and 1% levels, respectively. Robust t Statistics
are in parentheses. Standard errors are clustered by country. Time fixed effects are controlled in all regressions.

4.2. Student flow between developing countries
The asymmetry in economic conditions, education quality and social cultures is atypical characteristic between developing and developed countries. As a result, theincentives to study abroad are likely to vary across developing and developed origin
countries (Abbott and Silles, 2016). According to UN classification, we divide 102
sample countries into developing (36 countries) and developed countries (66 countries).
11

In subsequent analysis, we also add three bilateral variables: geographic distance(distance), common official language (language), and common religion (religion),
following Eq. (2).
We start by analyzing the link between cultural factors and cross-border mobility
between developing countries. The empirical results are shown in Table 5. In Models
2 and 6, the coefficients of cultural trade are positive, and are significant at the 1% level.
In Models 3 and 6, the coefficients of immigrants are significantly positive. However,in Model 6, the common official language shows a significant negative effect on the
introduction of international students, which is contrary to our expectation. In addition,
we find that educational factors in the host country are more important than economic
factors when students flow between developing countries. To be specific, thecoefficients of education expenditure on education as a percentage of GDP (expegdp)
and student-teacher ratio (stutea) are larger and more significant than those of relative
GDP per capita (gdpperratio). It can be interpreted that better education quality andmore scholarships are preferred by international students. In addition, the coefficient ofgeographic distance (lndistance) is negative and statistically significant at 1% level inall models, indicating that international students from developing countries prefer tostudy in nearby developing countries.
Overall, trade in cultural products and migrant networks are the two main cultural
determinants of international students who come from developing countries and study
in other developing countries. They tend to choose destinations near their homelands
that speak different languages. Also, the host countries’ financial expenditure andhuman resource investment on higher education cast important influences on thedestination choice of international students from developing countries.

Table 5: Student flow between developing countries
gdpperratio
lndistance
ppp

Model 1Model 2Model 3Model 4Model 5Model 60.0007-0.00002

0.00020.00070.0007-0.00002(1.15)(-0.05)(0.35)(1.20)(1.13)(-0.05)-0.5899***-0.3948***-0.2955***-0.5959***-0.5830***-0.2967***(-14.23)(-11.21)(-8.28)(-13.91)(-13.45)(-8.67)-0.02830.0012-0.0203-0.0295-0.0280-0.005112

expegdp
stutea(-1.55)(0.08)(-1.21)(-1.61)(-1.53)(-0.35)0.0756***0.0846***

0.0735***0.0759***0.0757***0.0841***(7.23)(8.69)(7.01)(7.27)(7.23)(8.51)-0.0118***-0.0122***-0.0107***-0.0116***-0.0120***-0.0100***(-5.36)(-6.52)(-5.25)(-5.25)(-5.40)(-5.34)0.1229***lncultrade

0.1093***(23.07)lnmigrant(19.99)0.1750***0.0960***(13.37)(7.70)

language-0.0517-0.3029***(-0.66)(-4.18)religion

0.0822-0.0823(0.70)(-0.82)

9.1721***5.7560***4.4171***

9.2697***9.0475***

4.2232***(13.86)(10.20)(7.76)(13.51)(12.93)(7.65)

Observations

49647

49647

49647

49647

49647

49647

R2

0.13320.30870.20420.13330.13330.3286

Constant

Note: *, **, *** indicate that the variable is significant at the 10%, 5% and 1% levels, respectively. Robust t Statistics
are in parentheses. Standard errors are clustered by country pairs. Time fixed effects and country fixed effects arecontrolled in all regressions. Unless otherwise explained, the following tables use the same notation.

4.3. Student flow from a developing country to a developed country
We next explore the impacts of cultural determinants on international students
flowing from developing origins to developed destinations. The empirical results arereported in Table 6.
In Models 2 and 6, the bilateral cultural trade, i.e., exports from developed
countries to developing countries, shows a positive effect on international student
mobility at the 1% level. However, in Models 4 and 6, the coefficients of common
religion are positive, but merely statistically significant at the 1% level in Model 6,
indicating that international students from developing countries tend to choosedeveloped countries which share a common religion as destinations.
In Models 3 and 6, migration networks have a very significant effect as a powerful
push factor for international student mobility. For many young people, studying abroad
is part of their life experience. In Pakistan, Thailand and Vietnam, studying abroad isconsidered as one way to promote the family’s social status; In the Philippines, West
Africa, South Africa and other regions, a large number of students have studied abroad;13

In Nigeria, 40% of college students have aspirations to study overseas to gain social
status (UNDP, 2009). 8 These countries have established cultures of international
mobility, and the presence of international migration networks leads to the growth ofsuch cultures and overall normalization of the mobility process.
In Models 5 and 6, the coefficients of a common official language are statistically
positive at 1% level, since students can more easily adapt to the local environment when
they study in countries and regions that share a common language. For example,
language similarity drives Latin-American students to choose Spain as their destination.
15% of the international students in Spain come from Latin America, while only 12%
in the USA come from those countries (D’ Costa, 2006), even though the U.S. is muchcloser physically. Notice the interesting contrast: in terms of language, international
students from developing countries tend to choose developing countries that are very
different, but they choose developed countries that are similar.
In addition, when students from developing countries flow to developed countries,
they are simultaneously affected by the economic and educational factors. Our results
show that all economic and educational variables are significant in almost all models.
On the one hand, students tend to choose countries with a relatively lower level of
economic growth (lower living costs), as can be observed from the significantly
negative coefficients of the gdpperratio variable in all models; On the other hand,international students from developing countries prefer to choose developed countries
with a lower proportion of education expenditures and a higher student-teacher ratio,
which may be easier for them to apply to. For example, the New Zealand National
College used to be officially accredited and recommended by the New Zealand
Qualifications Authority. These official qualifications together with low entry barriers
encourage many Chinese students to study in this college.9 Besides, the coefficient ofgeographic distance (lndistance) is positive and statistically significant at 1% level inModel 6, indicating that international students from developing countries prefer tostudy in remote developed countries.
89

UNDP: Human development report 2009, www.hdr.undp.org.
Zaobao, https://www.zaobao.com/realtime/china/story20180223-837439, February 23, 2018.
14

In sum, the exports of cultural products from developed countries to developing
countries and the migration networks between countries are significant determinants ofdeveloping country students, when choosing developed countries as destinations.
Students from developing countries tend to choose developed destinations that share acommon language. However, they tend to choose those developed countries with alower share of financial expenditure and a higher student-teacher ratio.
Table 6: Results of student flow from a developing to a developed country
gdpperratio

Model 1Model 2Model 3Model 4Model 5Model 60.0153***0.0025***0.00150.0130***0.0154***-0.0017*(11.90)(2.84)(1.32)(10.74)(12.02)(-1.88)-0.5060***-0.0500

0.3761***-0.5324***-0.4786***

0.3265***(-4.70)(-0.56)(4.05)(-5.02)(-4.41)(3.76)-3.5970***-1.4416***-1.5858***-3.3017***-3.5793***-0.9897***(-21.58)(-10.43)(-10.35)(-20.88)(-21.42)(-7.63)expegdp-0.1771***

0.0438**-0.1417***-0.1771***-0.1720***-0.0438**(-7.02)(2.03)(-5.58)(-7.08)(-6.80)(-2.05)stutea0.1401***

0.0694***0.0576***0.1057***0.1406***

0.0311***(8.48)(5.30)(4.50)(6.85)(8.46)(2.65)

lndistance
ppp

0.2138***lncultrade

0.1041***(33.73)(17.16)
0.4226***lnmigrant

0.2964***(31.68)(20.17)2.9037***

language

1.4184***(12.39)(9.69)
0.8909***religion

0.1582(3.54)(1.12)

11.1242***0.5660-4.5670***

11.4943***

10.3175***-4.9832***(6.25)(0.38)(-2.94)(6.53)(5.71)(-3.40)Observations

29172

29172

29172

29172

29172

29172

R2

0.33970.50880.5766

0.40680.34280.6216

Constant

4.4. Student flows between developed countries
In this section, we turn to investigate the role of cultural factors in determining thestudent mobility between developed countries. Table 7 presents the empirical results.
In Models 2 and 6, the coefficients of bilateral cultural trade (lncultrade) are
positive and statistically significant, showing that the cultural output among developed
countries has a positive effect on attracting their counterparts’ students. In Models 315and 6, the coefficients of the immigrant stock (lnmigrant) are significantly positive,
indicating that migration networks are important for student mobility between
developed nations. In Models 4 and 6, the coefficients of a common official language(language) are significantly positive, implying that international students fromdeveloped countries tend to study in other developed countries that share a common
language. In Models 5 and 6, the coefficients of common religion (religion) aresignificantly positive, indicating that international students from developed countries
tend to study in other developed countries that share a common religious belief.
In addition, the economic and educational factors are important determinants ofdeveloped country students choosing developed countries as destinations. However, incontrast to the flows from a developing to a developed country, the coefficients ofrelative GDP per capita (gdpperratio) in all six models are significantly positive,
indicating that international students from developed countries tend to choose thosedeveloped countries with a higher income level. Also, the education expenditure(expegdp) in the host countries also exerts a significant effect on the location choice ofinternational students. Further, the coefficient of geographic distance (lndistance) is
positive and statistically significant at 1% level in Model 6, indicating that international
students from developed countries prefer to study in other remote developed countries.
In general, the exports of cultural goods and international migration networks are
significant factors affecting the choices of developed country students to study in otherdeveloped countries. They tend to choose developed countries that share a common
language and a common religion belief.

Table 7: Student flow between developed countries
gdpperratio

Model 1Model 2Model 3Model 4Model 5Model 6

0.5390***0.2395***0.2491***0.5362***0.5417***0.1600***(9.87)(7.00)(6.52)(9.81)(9.86)(5.41)-0.0003-0.5918***-0.5451***

0.1974***lndistance-0.6284***(-8.72)(-0.59)(-0.00)(-8.48)(-7.20)(3.50)

ppp-3.6516***-0.8481***-1.1959***-3.3410***-3.5504***-0.1293(-12.67)(-3.54)(-4.66)(-11.61)(-12.15)(-0.59)-0.01130.3229***0.0956**-0.0202-0.0218

0.2563***(-0.29)(9.53)(2.47)(-0.51)(-0.58)(7.76)

expegdp-0.034516

stutea

0.1144***0.0739***0.02910.0885***0.1116***0.0204(4.35)(3.47)(1.35)(3.47)(4.19)(1.00)

0.3390***ln(cultrade)0.2038***(23.45)(16.01)
0.4926***ln(migrant)

0.3376***(21.24)(14.47)1.9885***

language

0.8030***(6.41)
religion

0.2730*(4.16)(1.68)-1.221213.2243***

12.4920***-7.7608***(-3.09)(-1.12)(10.33)(8.97)(-7.12)

14157

14157

14157

14157

14157

14157

0.30300.46940.49990.32790.31040.550214.0230***-3.5785***(10.61)
Observations
R2

Constant(3.33)
0.9655***4.5. Student flow from a developed to a developing country
We finally investigate how international students from developed countries todeveloping countries are affected by bilateral cultural factors, as listed in Table 8. The
regression results of Models 2, 3 and 6 show that the coefficients of the exports ofcultural goods (lncultrade) and the immigration stock (lnmigrant) are all positive andstatistically significant. However, in Models 4 to 6, the coefficients of the common
language (language) and common religion (religion) are negative and statisticallysignificant, implying that students from developed countries tend to choose developing
countries that speak different languages and have differentiated religious beliefs as
destinations, especially for non-degree seeking students.

Table 8: Student flow from a developed to a developing country
gdpperratio
lndistance
ppp
expegdp
stuteaModel 1Model 2Model 3Model 4Model 5Model 60.01610.01280.00400.02100.01470.0093(0.88)(0.96)(0.26)(1.12)(0.81)(0.70)-0.2077***-0.2016***-0.1533***-0.2127***-0.1705***-0.1098**(-3.57)(-4.03)(-3.14)(-3.66)(-2.62)(-2.27)-0.0366**-0.0205-0.0493***-0.0394**-0.0379**-0.0371***(-2.27)(-1.60)(-3.41)(-2.43)(-2.32)(-3.04)-0.0104

0.0008-0.0115-0.0097-0.0073

0.0043(-1.07)(0.10)(-1.21)(-1.00)(-0.78)(0.52)-0.0066**

0.0026(-2.34)(1.17)-0.0074***-0.0020-0.0045-0.0060**(-2.59)(-0.88)(-1.60)(-2.13)17

0.1008***

lncultrade

0.0864***(17.29)(14.06)0.1275***lnmigrant

0.0874***(9.45)(6.31)-0.3930***

language-0.5156***(-3.57)(-5.63)-0.4024***religion-0.6687***(-3.27)(-6.65)4.2870***3.1566***2.9650***4.4241***3.6823***

1.5862**(4.42)(3.87)(3.78)(4.56)(3.44)(2.04)

Observations

25194

25194

25194

25194

25194

25194

R2

0.07830.25570.14120.08260.08160.2847

Constant

4.6. Further analysis related to China
4.6.1. Cultural factors and inbound students in China
So far we have looked at global samples. In this subsection, we examine the caserelated to China. A survey in Beijing found that the interest in Chinese culture is themost important determinant of international students studying in China (Liu et al.,2013). To be specific, about 93% international students hold that they choose to study
in China due to their interest in traditional Chinese culture and the desire to learn more
about China. 10 The survey results confirm our conclusion that cultural factors
significantly affect the destination choice of international students, and it is especiallytrue for developing destinations.
Among various cultural factors, common language plays an obvious role indetermining international students to developing destinations which is consistent with
our empirical findings. With the rise of China's economic scale and international
influence, a "Chinese fever" has swept the world. In this context, the study of Chinese
language has become a necessary skill for personal development and businesscooperation for many countries. The employment prospects of international students
who are proficient in Chinese are more promising, which in turn encourages moreinternational students to come. In 2015, about 45.6 percent international students
majored in the Chinese language, which is nearly four times that in western medicine,10

Details of the survey data can be found in Table 1 of Liu et al. (2013).
18

the second largest major. More detailed statistics show that about 85.82 percent students
majored in the Chinese language are non-degree seeking, indicating their urgent needto learn the language.11
Also, Wei et al. (2018) find that cultural factors, such as linguistic difference andmigration networks have significant effect on attracting international students to China.
Further comparison reveals that the effect is stronger for students from developed
countries than from developing countries. Qu et al. (2016) show that the improvementof cultural acceptance, measured as cultural products export to the origin countries,
plays an important role in promoting the scale of international students in China.
4.6.2. Cultural factors and outbound students from China
As for outbound students from China, several surveys confirm our empiricalconclusions derived from the global perspective in the previous sections. To be specific,
a survey conducted by Mycos, China’s first professional higher education management
and consulting company, shows that about 27.1 and 19 percent of Chinese students
studying in Europe and Oceania consider learning different cultures as the primaryreason for their destination choice in 2015. In contrast, only 13.8 percent of Chinese
students in other Asian countries regard learning cultures as the primary reason for theirdestination choice.12 This comparison shows that the influence of cultural factors on
Chinese outbound students is smaller in developing countries than in developed
countries, which is also consistent with our empirical results.
Another interesting finding is that social networks have an underestimatedinfluence on the destination choice of Chinese outbound students. Overall, about 13.9
percent of interviewees believed that social network, as measured by therecommendation from friends, relatives, teachers and alumni, was the primary
information channel, and it has become the third largest information source for Chinese
students in 2015.13 However, the impacts of social networks vary among destinations.

11

The data are derived from China Ministry of Education and calculated by the authors.
Details of the survey data can be found on p. 51 (Table 13), Annual Report on the Developmentof Chinese Students Studying Abroad (2016).
13 Details of the survey data can be found on p. 49 (Table 10), Annual Report on the Developmentof Chinese Students Studying Abroad (2016).
12

19

It has exerted the largest impact on Chinese students studying in neighboring Asian
countries, while the impact is relatively weaker in developed countries. Specifically,
about 20.7% of Chinese students in Asian countries select social network as the primary
information channel. In contrast, those who regard social network as the most important
information channel account respectively 11.3, 11.2 and 10.2 percent of total Chinese
students in Oceania, Europe and North America.14
5. Robustness Checks
5.1. Lagged explanatory variables
In order to avoid possible endogeneity caused by reverse causality, we now use
lagged explanatory variables to check the robustness of our results. Note that two of thevariables, common official language (language) and common religion (religion), aretime invariant, and they are not affected by the international students. Here we adopt
one-period and two-period lagged explanatory variables of bilateral cultural trade(lncultrade) and immigrant networks (lnmigrant). The regression results are reported
in Table 9, confirming that our findings are robust. The results show that the export ofcultural products is conducive to the increase of international students. Migrant
networks are also beneficial for attracting students from the origin countries. However,
the impact of time-invariant cultural determinants depends on the types of origin
countries and destination countries. To be specific, the common official language
significantly increases the international students to developed destinations. By contrast,
international students prefer to choose developing destinations whose official language
is different from their origin countries. The contrast comes from the fact that students
studying in developed countries are more focused on degree courses or improving theirliving standards via future immigration, while in developing countries, they mainly aim
at learning unique foreign languages and experiencing differentiated cultures.
Common religion is an important cultural determinant for students from developed
countries, and it significantly increases the students from developed countries to study

Details of the survey data can be found on p. 55 (Table 19), Annual Report on the Developmentof Chinese Students Studying Abroad (2016).
14

20

in another developed destination. By contrast, international students from developed
countries prefer to choose developing destinations whose religious belief is different
from their origin countries. This result confirms our previous finding that international
students studying in developing countries aim at experiencing differentiated cultures,such as foreign languages and religious beliefs.

Table 9: Robustness checks using lagged explanatory variables
global sample

lngdppert-1

t-2

0.17570.1768(0.62)(0.62)

gdpperratio

lndistance

ppp

expegdp

stuteaL.lncultrade

L.lnmigrant

Between Developing

Developing to

Between Developed

Developed toCountries

Developed Countries

Countries

Developing Countries

t-1

t-2

t-1

t-2

t-1

t-2

t-1

t-2

0.00010.0002-0.0020**-0.0023**

0.1593***0.1541***0.01400.0177(0.31)(0.37)(-2.18)(-2.47)(4.87)(4.31)(1.06)(1.21)-0.3009***-0.2962***

0.3224***0.3276***

0.1931***0.2037***-0.1115**-0.1073**(-8.73)(-8.58)(3.73)(3.81)(3.43)(3.59)(-2.31)(-2.23)-1.1183***-0.1219-0.0768-0.0383***-0.0397***0.00120.0012-0.00210.0002-1.0641***(1.42)(1.45)(-0.15)(0.01)(-7.23)(-6.85)(-0.55)(-0.34)(-3.13)(-3.25)0.3269**0.3052**0.0856***0.0861***-0.0415*-0.0394*

0.2532***0.2582***0.00590.0073(2.27)(2.11)(8.75)(8.89)(-1.92)(-1.81)(7.60)(7.68)(0.72)(0.88)-0.1421***-0.1485***-0.0104***-0.0112***0.0242**0.01550.0103-0.0001

0.00250.0020(-2.77)(-2.83)(-5.44)(-5.72)(2.03)(1.28)(0.50)(-0.01)(1.07)(0.84)

0.4178***0.1111***0.1042***0.2023***0.0868***(2.70)(20.05)(17.05)(15.71)(14.10)0.3044

0.0970***0.2965***0.3326***0.0884***(1.25)
L2.lncultradeL2.lnmigrant(7.70)(20.07)(14.14)(6.38)

0.4125**0.1132***0.1040***0.2040***0.0878***(2.64)(20.11)(16.72)(15.80)(14.19)0.30800.0986***

0.2978***0.3240***0.0903***(1.25)(7.75)(20.05)(13.78)(6.47)

languagereligion-0.3025***-0.3083***

1.4161***1.4196***0.8184***0.8527***-0.5285***-0.5518***(-4.17)(-4.26)(9.69)(9.64)(3.38)(3.49)(-5.72)(-5.86)

0.2866*-0.6750***-0.6803***-0.0911-0.0752

0.15900.16980.2717*(-0.90)(-0.73)(1.13)(1.19)(1.67)(1.75)(-6.73)(-6.69)-6.1664**-5.7044*4.2789***4.2601***-4.7651***-4.6142***-7.2634***-7.1632***

1.6240**1.6376**(-2.03)(-1.86)(7.67)(7.61)(-3.25)(-3.15)(-6.66)(-6.48)(2.10)(2.11)

Observations

1116

1056

47503

45024

27744

26180

13464

12705

24106

22848

R2

0.36800.36790.33090.33210.62260.62250.5446

0.54150.28620.2882

Constant

5.2. Alternative dependent variables
21

To deal with zero values of the dependent variable, we add one to international
student mobility in the baseline estimation before taking logarithm. In the robustness
checks, we use another log-like transformation (see Liu and Qiu, 2016). It is calculatedas, lnstudentijt  ln[ studentijt  ( studentijt 2  1)1 / 2 ] . Table 10 reports the estimation
results, again confirming our findings.

Table 10: Robustness checks using alternative dependent variables
BetweenDeveloping to

BetweenDeveloped to

Developing

Developed

Developed

Developing

Countries

Countries

Countries

Countries

(2)

(3)

(4)

(5)

0.0001-0.00140.1775***0.0164(0.16)(-1.36)(5.57)(1.05)-0.3333***

0.3644***0.2418***-0.1279**(-8.71)(3.95)(4.04)(-2.38)0.0013-0.0006-1.1184***-0.0298-0.0377***(1.39)(-0.04)(-7.88)(-0.13)(-2.71)0.3635**0.0979***-0.0391*

0.2927***0.0079(2.33)(8.82)(-1.66)(8.13)(0.82)-0.1439**-0.0118***

0.0288**0.01240.0032(-2.60)(-5.54)(2.22)(0.56)(1.24)

0.4289***0.1255***0.1197***0.2279***0.1009***(2.64)(20.59)(17.81)(16.42)(14.79)0.30700.1059***0.3234***0.3544***0.1010***(1.19)(7.68)(20.22)(14.06)(6.57)-0.3467***

1.4992***

0.7839***-0.6009***(-4.30)(9.67)(3.07)(-5.81)-0.08240.16100.3235*-0.7759***

global sample(1)
lngdpper0.2124(0.71)

gdpperratio
lndistance
ppp
expegdp
stutealncultradelnmigrant
languagereligion(-0.73)(1.03)(1.86)(-6.78)-6.6174**4.7348***-5.5079***-8.7713***

1.8399**(-2.09)(7.68)(-3.55)(-7.57)(2.13)

Observations

1169

49647

29172

14157

25194

R2

0.35100.33010.62300.5397

0.2913

Constant

5.3. Alternative measurement of cultural factors
Hofstede (2010) proposes a framework of six cultural dimensions by which we cancompare the cultural distance between two countries: power distance, uncertainty
22

avoidance, individualism, masculinity, long-term orientation and indulgence. The index
of cultural distance is calculated as,
CDij   I ci  I cj  nVc
n

2

(3)

c 1

In the above formula, CDij represents the cultural distance between countries i andj; Ici and Icj show the index value of dimension c in countries i and j; n is the number ofcultural dimensions; Vc is the variance of cultural dimension c.
However, the data of cultural distance are not available for all countries, especiallyfor some developing countries. To avoid the bias of sample selection, we have not used
this index in the baseline regression. Here we add it to our econometric model andconduct another robustness check. The results are shown in Table 11, basicallyconfirming robustness, except for Model 6, where the common religion is positive but
statistically insignificant, indicating that a common religion cannot significantly
promote the mobility of international students between developed countries.
Table 11: Robustness checks using alternative cultural factor measurements
Between Developing

Developing to

Between Developed

Developed toCountries

Developed Countries

Countries

Developing Countries(1)
gdpperratio
lndistance
ppp
expegdp
stutealncultradelnmigrant
languagereligion

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.1535***-0.1668*-0.1769**-0.0147-0.0115-0.0024-0.0001

0.1518***(-0.56)(-0.48)(-0.52)(-0.02)(5.01)(5.03)(-1.87)(-1.97)-0.7167***-0.8580***0.3299**0.23310.2273***0.2249***-0.4406***-0.3666***(-6.85)(-7.55)(2.16)(1.54)(4.18)(3.98)(-4.85)(-3.65)-0.0707-0.0859-1.1144***-1.1672***-0.3064-0.2651-0.1276***-0.1375***(-1.30)(-1.58)(-5.64)(-5.89)(-1.42)(-1.22)(-3.41)(-3.75)0.2013***0.2219***-0.0311-0.0372

0.3222***0.3139***0.0927***0.1019***(4.90)(5.55)(-0.76)(-0.92)(9.52)(9.50)(3.03)(3.35)-0.0393***-0.0255**

0.0419*0.03040.01770.0099-0.0234**-0.0156*(-3.22)(-2.10)(1.93)(1.44)(0.86)(0.48)(-2.52)(-1.70)0.1273***0.1266***

0.1137***0.1148***0.2179***0.2179***0.1349***0.1252***(12.41)(12.25)(11.53)(11.72)(15.28)(15.37)(12.69)(11.13)0.0593**0.0782***0.3824***0.3537***

0.3326***0.3208***0.0428**0.0699***(2.27)(2.93)(16.85)(15.46)(14.41)(13.50)(2.10)(3.21)-0.8830***

1.3803***0.7544***-0.6077***(-3.00)(5.55)(2.97)(-2.88)-0.5179**0.24680.0908-0.6799***(-1.99)(1.26)(0.53)(-3.25)23

0.00610.00440.0066***0.0081***-0.0029*-0.0015

0.00270.0036(1.58)(1.18)(3.34)(4.05)(-1.75)(-0.86)(0.97)(1.28)

11.4840***

13.3820***-4.5745**-2.9276-8.2072***-8.1759***6.7088***5.4472***(6.74)(7.31)-0.0024(-1.28)(-7.64)(-7.31)(4.65)(3.42)

Observations

9044

9044

11977

11977

13216

13216

10659

10659

R2

0.35420.36830.60730.61680.55030.55360.31260.3126

culdistConstant

6. Concluding Remarks
In contrast to the previous literature, we empirically analyzed the role of cultural factors
in shaping the cross-border mobility of international students, using the data of 102
countries from 2000 to 2015. We have used the export of cultural goods as a proxy forculture factors, perhaps for the first time in the literature. Our results indicate that
culture factors are critical determinants of international student mobility. The export ofcultural products is conducive to the increase of international students, in addition tointernational migration networks that have been found important in existing studies(Beine et al., 2014; Beech, 2015). However, the impacts of a common language and acommon religion depend on the types of origin countries and destination countries. To
be specific, international students choose to study in developing countries whose
official language and religious beliefs are different from their home countries, while
they tend to go to developed countries with common languages.
These results are relevant for the design of policies to attract foreign students,
especially relevant for developing countries, such as China’s B&R Initiative. We
propose that policy makers value the role of cultural factors in introducing international
students. First, it provides an effective way to attract international students around theworld, by expanding the exports of cultural goods. Second, the government could
establish migration networks which can act as an information and cultural bridge
between the destinations and origin countries. Third, it is especially critical fordeveloping countries to cultivate their “soft power” such as unique customs, linguistic
and religious cultures, raising their attractiveness to international students. Last but not24least, as for developed countries, enhancing the popularity and global influence of theirown languages are also effective.
We have devoted special attention to China, which has become the 3rd largest hostof international students in higher education and the biggest developing destination.
Most foreign students come from South Korea, the U.S. and Southeastern Asian nations,and in recently years, more and more are coming from countries along the “belt & road”
due to government scholarships. As for Chinese outbound students, most of themchoose English-speaking countries, especially the U.S., and neighboring countries
Japan and South Korea. The former is due to the fact that English is the de-factor
international language, and the latter for the business opportunities the two neighbors
provide through their investments in and trade with China.
Finally a note of caution. We have not differentiated between the students fordegree and those for non-degree studies, for two reasons. First, many developing
countries in our sample lack the data for non-degree seeking students; Second, thenumber of non-degree seeking students in some developing countries is very small,which hardly makes sense in the regression analysis. As data become available in the
future, research on them would be very fruitful.

25

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