Determinants of international student mobility

High Educ DOI 10.1007/s10734-012-9593-5

An empirical study on the determinants of international student mobility: a global perspective Hao Wei

Ă“ Springer Science+Business Media Dordrecht 2012

Abstract This paper, based on the data of 48 countries and regions from 1999 to 2008, studies the economic and educational determinants of how countries of different types attract international students. The study finds that: the volume of merchandise trade between countries facilitates international student mobility across borders; international students from developing countries put the same weights on educational and economic factors for peer developing countries as potential destinations, while only economic factors are taken into consideration for developed countries as potential destinations. On the other hand, international students from developed countries often value educational factors more for developed countries as possible destinations, while equally weigh educational and economic factors for developing countries as possible destinations. Therefore, countries aiming to attract talents from other countries should pay more attention to attract international students and encourage them to seek working opportunities in local employment markets after finishing study.

Keywords International talents flow International students Outflow country Inflow country

The mobility of talent (international students) across borders has emerged as an important field of study that various nations and organizations have been attaching great importance to. With the advent of the era of knowledge economy, talent has become the key driving force of economic development and the guarantee of improvement of national strength.

The higher speed of globalization has accelerated the international flow of production factors, while the flow of talent, as an important component of production factors and resources, has become prevalent around the globe. Since international students of higher education level (service trade of higher education) is an important part of the international talent flow, any country should attach great importance to the international talent flow and H. Wei (&) School of Economics and Business Administration, Beijing Normal University, No.19 Xinjiekou Wai Street, Beijing 100875, China e-mail: weihao1006@gmail.com

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the service trade of higher education to attract talents, so as to transform its economic growth pattern, optimize its trade structure and strengthen its economic status in the world.

The paper, from the perspective of international talent flow, studies the determinants of the flow for countries of different types, which shall offer strategic reference for various countries in the process of policy making.

Introduction In recent years, a clear trend of cross-border talent mobility has gradually shown its great significance among all the international economic activities, with its scale and complexity still rapidly growing (OECD 2008). Nations around the globe have noticed this issue and have been trying their best to compete for high-end foreign talent. Developed countries, including the US, UK, France, Germany and Japan, have enacted new bills that favor immigration and employment of highly-skilled foreign talents while putting more restrictions on unskilled labor. Developing countries such as China, India, Mexico, Brazil, South Korea and Indonesia have also issued favorable policies in a bid to create a reverse flow of foreign-educated citizens as well as to attract talents from overseas. According to ‘‘Human Development Report 2009’’ issued by UNDP (United Nations Development Programme), 92 % of developing countries and 100 % of developed countries are in favor of a temporary inflow of skilled workers, 62 % of developing countries and 93 % of developed countries support a permanent inflow of skilled workers, while only 38 % of developing countries and 50 % of developed countries are against such a permanent inflow of unskilled workers.

Therefore, international talent flow has recently evolved into new patterns. For the past decades, the ‘‘old’’ pattern that has been dominating cross-border talent flow is one-way, from developing countries to developed countries and from secondary developed countries to leading developed countries. However, a ‘‘new’’ pattern that features a two-way flow is emerging as developing countries are attracting talent from developed countries with their improved economic conditions, standardized legislations, narrowing gap with developed countries, and more growth opportunities. Moreover, secondary developed countries have also enhanced their competitiveness by fitting the economic development strategies and policies to be more talent-friendly. In short, the prevailing pattern of international talent flow has evolved into a two-way pattern, flow between developed countries and developing countries, and flow between secondary developed countries and leading developed countries. In other words, the international talent flow has transformed from the pattern of oneway outflow into the pattern of talent circulation (OECD 2008). In this fast-changing climate, a growing range of international-student source countries are rapidly transforming into destination countries. For example, China, Singapore, and Malaysia exemplify this trend. By 2006, China had become the sixth most popular destination, enrolling 141,000 international students, the majority drawn from Asia but also with growing numbers from OECD countries (Hawthorne 2008). The potential of international students is large and growing, by 2005, 2.7 million students of higher-education level were enrolled abroad, compared with just 600,000 in 1975, the demand is predicted to rise to 7.2 million by 2025(Bohm 2003). In a word, traditional flows of talents between countries that tended to go from the south to the north are reversed, a motion of talent is going in all directions: north–north, north–south, south-north, and south–south (Mahroum 2000).

In the international competition for talents, countries, either developing or developed, are facing tough challenges on how to design the best-fit policies to compete for talents, to

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make up for the talent gap in the process of national economic development and innovation (OECD 2008). This leads to some questions that need to be addressed. What are the major determinants for international talent flow? What are the determinants for different types of countries to attract talents? What are the differences between the determinants for developing countries and developed ones? This research from these perspectives shall no doubt offer theoretical evidence and reference for governments in making related policies.

However, there are only limited studies and literatures in such a field. The existing studies are mainly on the international flow of commodities and capital. Theoretical studies on talent mobility can be very challenging due to the broad definition and classification of talent. Therefore, this paper takes international students as the sample in this empirical study, representing international talent, and based on the data of 48 countries and regions from 1999 to 2008, carefully examines the determinants for different types of countries to attract foreign talents.

Related definitions and literature review Basic concepts Actually, international students used to be regarded as customers rather than talents in inflow countries. For example, international students belong to Consumption Abroad in GATS (General Agreement on Trade in Service) of WTO (World Trade Organization).

However, international students are more and more likely to be regarded as talents now.

The number of foreign student enrolments has gone up in all countries as the total number of enrolments is growing, and current trends show that the mobility of people with tertiary education (a proxy of talent) is higher than that of the people with lower levels of education(Aimee Rindoks 2010). The international mobility of students is an important aspect of the internationalisation of HRST (human resources in science and technology), which acts as a vital complement to the transfer of knowledge through flows of goods and capital across borders (OECD 2008). Host countries are often the main beneficiaries of foreign students’ inflows as stay rates are often quite high. For instance, about 50 % of European students after finishing their PhD studies in the United States stay for longer time or even permanently (Finn 1997), 30 % of the foreign graduates from the universities in northern California stay and work in Silicon Valley (ACTEAM 1999). OECD countries benefit from the inflow of talented students, and many now actively seek to attract foreign students (OECD 2008).

Sami Mahroum (2000) divided international talent into five groups, they are: (i) Managers & Executives, (ii) Engineers &Technicians, (iii) Academics & Scientists, (iv) Entrepreneurs, and (v) Students, and Students are considered as main supply channels to these groups. At the same time, Aimee Rindoks (2010) hold that international talent composes of three types, (i) directly productive talent, (ii) academic talent, (iii) talent in social and cultural sectors, academic talent includes the mobile international students.

In general, talent is a very broad concept. There are enormous problems associated with gathering and finding data on the mobility of all types of talent. So this paper is not trying to study all types of talent, but instead focuses on a small segmentation—students, or to be more concise, international students of higher education level. Such students are chosen as the main study object of this paper because: (1) international students of higher education level are a key component of international talent and the flow, the study of which can properly represent a broader population; (2) compared with international students, other

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international talent flow of non-student types tend to be less mobile and relevant data is scarce.

According to UIS (UNESCO Institute for Statistics), foreign or mobile students are defined as those who study in foreign countries where they are not permanent residents.

Code of Good Practice in the Provision of Transnational Education initiated by UNESCO (United Nations Educational, Scientific and Cultural Organization) and the EU defines transnational education as ‘‘all types of higher education study programs, or sets of courses of study, or educational services (including those of distance education) in which the learners are located in a country different from the one where the awarding institution is based.’’ The transnational education mainly flourishes in the field of higher education, and hence this paper focuses its empirical studies on ‘‘transnational higher education’’.

Meanwhile, according to ISCED 1997 (International Standard Classification of Education) as approved by the UNESCO General Conference at its 29th session in November 1997, general education is categorized into seven different levels: Level 0—Pre-primary education, Level 1—Primary education or first stage of basic education, Level 2—Lower secondary or second stage of basic education, Level 3—(Upper) secondary education, Level 4—Post-secondary non-tertiary education, Level 5—First stage of tertiary education, and Level 6—Second stage of tertiary education. The higher education as studied by this paper includes both level 5 and level 6.

Literature review In the past several decades, the research on international talent flow has moved beyond theory to empirics, new data have emerged that allow researchers to accurately measure skilled-worker movements, explore how they relate to development outcomes (Clemens 2009), and focus on the determinants and mechanism of international talent flow etc.

The current researches, empirical studies of the determinants of international talent flow, are generally based on one country and its international students, either outflow or inflow, and only a few of papers involve studies from a global perspective. McMahon (1992) notes that the number of international students of an inflow country closely relates to the economic integration between countries, the economic development and the education level of an outflow country. Chen and Barnett (2000) find that international talent flow is impacted by geopolitical and economic factors, such as the economic integration between countries and political alliances during the ‘‘Cold War’’, etc. Mazzarol and Soutar (2001) point out that the quality of higher education, the desire to learn about a foreign culture, and the immigration possibilities after graduation are the main determinants.

Lindsay and Pramod (2001) think that the per capita income gap between the outflow and inflow countries, exchange-rate adjusted tuition level, and the education quality of outflow countries are determinants. Mazzarol and Soutar (2002) again point out that the determinants also include the students’ understanding about foreign educational institutions and programs, their familiarity with the foreign culture, and connections between the two societies. Racine et al. (2003) note that the connections in terms of language and culture between the two countries are very important as well.

Recently, more and more studies pay attention to the determinants of international student mobility flows. DeVoretz (2006) points out that tuition fees, the language of instruction and the quality of the higher education institutions affect international student mobility. Agasisti and Dal Bianco (2007) find that the number of faculties, the resources invested in student aid, and the socio-economic conditions of the area have a positive impact on the competitiveness of a university, the distance from the area of residence to the

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destination plays a deterrent role. Liu and Wang (2008) believe that tuition, government support for education, and population size of the younger generation in outflow countries are crucial to international talent flow. Van and Veugelers (2009) find that the quality of a country’s higher education system has a positive and significant effect on the size and direction of flows of students, at the graduate level, however, the driving force for student mobility appears to be the lack of educational opportunities in the home country. Capuano (2009) holds that educational expenditures of outflow countries play an important part in the flow, a larger amount of education expenditure shall cut down on the number of outflow talent, the per capita income fluctuations in inflow countries discourage international talent inflow, as they prefer economically stable and immigrant-friendly countries. Brezis and Soueri (2011) point out that educational quality outweighs income level in the students’ decision making process. Rodrı´guez Gonza´lez et al. (2011) points out that country size, cost of living, distance, educational background, university quality, the host country language and climate are all found to be significant determinants. Kahanec and Kra´likova´ (2011) conclude that among higher education policies especially the quality of higher education institutions and the availability of programs taught in the English language can act as an important tool to attract international students.

Moreover, some scholars have studied the relationship between international trade and talent flow, and find that the two are complementary, and both as well as foreign investments are getting more inter-dependent (Poot and Strutt 2009). One reason is that international trade as a bridge of commercial exchange between outflow countries and inflow countries facilitates talent flow by indirectly lowering the flow costs (Pederson et al. 2004).

On the other hand, talent flow also helps promote international trade as the outflow talents have a preference for products from their home countries and their natural understanding of their home countries shall lead to lower trade costs (Head and Ries 1998).

In short, it can be seen that determinants of international talent flow vary greatly from each other, including but not limited to economic, educational, cultural, and even individualistic factors etc. According previous studies and researches, economic factors and educational factors are two of the most influential determinants, therefore, in this study, the focus will be placed on these two categories.

From above, it can be seen that, among the empirical studies, almost all of the study objects are limited on one single country and only a few of the study objects have a real global focus, even if some papers have done research from the global perspective, they have not classified the countries into different types. This paper tries to explore this new perspective and takes a deep look at the determinants of international talent flow for different types of countries, based on data of 48 countries and regions from 1999 to 2008.

The relevant empirical studies are conducted from five aspects: an integral view from all countries, flow between developing countries, flow from developing countries to developed countries, flow from developed countries to developing countries and flow between developed countries.

Selection of variables, perspective of study and source of data Selection of variables From the existing relevant studies, economic factors and educational factors are the most important in influencing the international talent flow. Therefore this paper studies the flow of different types of countries in terms of the impacts of economic factors and educational

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factors, with other relevant macro elements given consideration. The key explanatory variables selected by this paper are as follows: Economic factors The economic development level, gap and relations of and between inflow and outflow countries, are crucial determinants in the students’ decision making process. (1) ‘‘Per capita GDP of the inflow country’’ or ‘‘per capita GDP gap’’ reflect the economic gap between countries, with the former representing the development level of education, research and public welfare of the inflow country, and the latter representing the gap in such categories between the two countries. The higher the ‘‘per capita GDP of the inflow country’’, the bigger the ‘‘per capita GDP gap’’ and the deeper the impacts on the students decisions will be. The international talent flow is fundamentally a process of cost-based decision-making process, in which process students tend to choose a country better developed than the home country. However, economic factors may impact students from various types of countries differently, normally positive, but possibly negative under certain circumstances. (2) ‘‘Bilateral trade volume in goods’’ is a good gauge for the economic connections of two countries, and thus trade relations between inflow and outflow countries as well. Stronger economic connections are generally positive factors as they indirectly lower the costs of the flow. In other words, bigger trade volume smoothes the talent flow.

Education factors ‘‘The education level of the inflow country’’, as a key determinant for the international talent flow, is analyzed in this paper through studying three selected variables, ‘‘student– teacher ratio’’, ‘‘higher education expenditure as percentage of GDP’’ and ‘‘higher education enrollment rate in the inflow country’’. Generally speaking, (3) a lower ‘‘student– teacher ratio’’ is a positive factor as it indicates the abundance of faculty resource, and higher quality of education, which guarantees more attention to the international students.

(4) A higher ‘‘higher education expenditure as percentage of GDP in the inflow country’’ suggests a higher priority of higher education in the inflow country. Therefore, educational institutions in such countries are generally ‘‘cash-rich’’, and capable of offering a favorable education environment and research facilities, and possibly generous in granting scholarships. (5) A ‘‘higher education enrollment rate in the inflow country’’ indicates more attention to domestic students and possible negligence to international students, but it may vary for different types of countries. Meanwhile, a larger amount of domestic students represents a larger number of domestic graduates, thus affecting the job market for international talents.

Other factors Other determinants worth studying are the variables of ‘‘related costs in the inflow country’’, ‘‘the demand for labor in the inflow country’’ and ‘‘the level of information development in the inflow country’’ Generally higher ‘‘related costs in the inflow country’’(including tuition and living expenses etc.) may cut down on the number of international students, preconditioned all other factors being the same; meanwhile a stronger ‘‘demand for labor (talent) in the inflow country’’ shall bring more favorable policies to

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attract international talents, thus resulting in a larger number of international students. A higher ‘‘level of information development in the outflow country’’ represents better access to studying-abroad information for the domestic students. However, unlike economic and educational factors, there is no easy way to quantify these factors, as there exist no readily available indicators. Consequently, the following three indicators are selected as proxies: (6) ‘‘purchasing power parity of the inflow country’’ for the relevant living expenses of the country, (7) ‘‘GDP per capita growth rate of the inflow country’’ for the demand of labor or talent of country, and (8) the ‘‘number of patents approved in the outflow country’’ for the information technology development level of the country.

Perspective of research The paper studies the international talent flow of different types of countries during the time period from 1999 to 2008, and classifies the selected countries into two groups of ‘‘the integral’’ and ‘‘the categorized’’ for the regression analysis. For ‘‘the integral’’ regression analysis, 48 countries and regions are selected. For ‘‘the categorized’’ analysis, 7 inflow countries with their respective top 7 outflow countries are selected. These categorized flows include the flow between developing countries, the flow from developing countries to developed countries, the flow from developed countries to developing countries and the flow between developed countries. According to the classification standard of the UN, developed countries include Canada, the US, original EU members, Australia, New Zealand, Japan, Israel and South Africa. Countries outside this group are all classified into developing countries.

For the integral analysis, it is the top 48 countries and regions selected under the condition that its data is available and complete during 1999–2008. For each categorized analysis, it is the top seven developing (developed) countries selected under the condition that its data is available and complete during 1999–2008.

Source of data The data used for this paper mainly comes from the databases and websites of UN (United Nations), UIS (UNESCO Institute for Statistics), OECD, IIE (Institute of International Education) and ILO (International Labour Organization) and adjustments have been made to the data as required by the study.

Empirical analysis Integral analysis: from the global perspective 48 major inflow countries and regions in Table 1 are selected in the global range as the data source for the integral analysis.

The selected dependent variable is the ‘‘number of international students of a country’’, and the explanatory variables are (1) ‘‘trade volume in goods with other countries (tra)’’, (2) ‘‘per capita GDP (gdpper)’’, (3) ‘‘higher education expenditure as percentage of GDP (expegdp)’’, (4) ‘‘student–teacher ratio in higher education (stutea)’’, and another three variables as (5) ‘‘growth rate of per capita GDP (gdprat)’’,(6) ‘‘higher education enrollment rate (enratio)’’ and (7) ‘‘purchasing power parity (ppp)’’. However, it is worth noting that

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the origins of the international students in the selected inflow countries have not been classified. Therefore, the explanatory variables are all relevant indicators of the inflow countries. This paper preliminarily suggests that ‘‘student–teacher ratio in higher education (stutea)’’ and ‘‘purchasing power parity (ppp)’’have negative impacts on the ‘‘number of international students’’, with the other five variables on the contrary.

Consequently, the regression equation constructed for the integral analysis is: mobstu ¼ C0 þ C1 tra þ C2 gdpper þ C3 exp egdp þ C4 stutea þ C5 enratio þ C6 ppp þ C7 gdprat The regression results are illustrated in Table 2. In Pattern 1, the variable of ‘‘trade volume with other countries’’ is significant at the level of 1 % and the variable of ‘‘higher education expenditure as percentage of GDP’’ at the level of 10 %, with the other five showing no significance. In Pattern 2, 3 and 4, the three newly added variables as ‘‘higher education enrollment rate’’, ‘‘purchasing power parity’’ and ‘‘growth rate of per capita GDP’’ also show no significance, while the variable of ‘‘trade volume with other countries’’ is significant in each pattern and ‘‘higher education expenditure as percentage of GDP’’ significant at the level of 10 % in Pattern 4. From the regression of all the explanatory variables, ‘‘trade volume with other countries’’ is the only explanatory variable that has been significant, and thus has a positive correlation with the number of international students of a country. This result is within comprehension, as the trade volume in goods between countries reflects their respective economic relationship, and hence a stronger connection facilitates exchange of education and the development of service trade in higher education. Meanwhile, the explanatory variable of ‘‘higher education expenditure as percentage of GDP’’ has been significant and in positive correlation with the flow under certain circumstances, which suggests that the financial support from the government plays a crucial part in attracting international students. Although the other factors have not shown any significance in this analysis, their symbols for coefficients coincide with the preliminary assumption, that ‘‘student–teacher ratio in higher education(stutea)’’ and ‘‘purchasing power parity (ppp)’’ have negative effects on ‘‘the number of international students of a country’’, with the other five variables on the contrary.

Categorized analysis 1:the flow between developing countries The seven selected developing countries as inflow countries are Belarus, South Korea, Turkey, Poland, Cuba, Romania and the Czech Republic, for each of which, the top seven developing countries as outflow countries are selected respectively, as listed in Table 3.

The econometric equation of categorized regression formulated in this study is: Table 1 48 Major countries and regions selected in the global range China

New Zealand

Chile

Germany

Sweden

Hong Kong, China

The Philippines

Cuba

France

Switzerland

Turkey Austria

Macau, China

Thailand

Mexico

Italy

Ice Land

Denmark

India

Malaysia

USA

Holland

Ireland

Romania

Australia

Vietnam

Canada

Belgium

Norway

Poland

Japan

Saudi Arabia

Russia

Spain

Greece

Hungary

Morocco

Egypt

Algeria

Madagascar

Cyprus

Bulgaria

South Korea

Brazil

GB

Portugal

Finland

Malta

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mobstu ¼ C0 + C1 bitra + C2 gdpperdif + C3 exp egdp + C4 stutea þ C5 enratio + C6 ppp + C7 tech In this equation, (1) the dependant variable is the ‘‘number of international students of a country (mobstu)’’; (2) the fundamental explanatory variables are ‘‘bilateral trade volume in goods (bitra)’’, ‘‘per capita GDP gap between countries (gdpperdif)’’, ‘‘higher education expenditure as percentage of GDP of inflow countries (expegdp)’’, ‘‘student–teacher ratio of higher education of inflow countries (stutea)’’, ‘‘the number of patents approved in the outflow countries (tech)’’, ‘‘Higher education enrollment rate in the inflow countries (enratio)’’, and ‘‘purchasing power parity of the inflow countries (ppp)’’.

From the empirical results in Table 4, ‘‘bilateral trade volume in goods’’ has been significant at the level of 1 % in each of the five patterns, coherent with the integral analysis. Meanwhile, ‘‘higher education expenditure as percentage of GDP’’ is also significant in Pattern 1, the newly added variable of ‘‘higher education enrollment rate in the inflow country’’ at the level of 10 % in Pattern 2, the variable of ‘‘per capita GDP gap between the inflow and outflow countries’’ at the level of 1 % in Pattern 3 with a positive effect on ‘‘the number of international students’’, and ‘‘higher education expenditure as percentage of GDP in the inflow country’’ at the level of 10 % with a positive correlation with ‘‘the number of international students’’ in Pattern 4. In Pattern 5, with each variable taken into consideration, ‘‘per capita GDP gap between countries’’ shows significance at the level of 1 % and is in positive correlation, which coincides with the expectation of the study and the theoretical research, The variables of ‘‘student–teacher ratio in higher education of the inflow country’’ and ‘‘higher education enrollment rate of the inflow country’’ have both been significant at the level of 1 %, with an unexpected positive effect from the former one. The reason could lie in the fact that developing countries are comparatively in shortage of the educational resources and thus the possibilities of studying-abroad rather than

Table 2 The integral regression results from the global perspective Variables

Pattern 1

Pattern 2

Pattern 3

Pattern 4

Pattern 5

cons

4134.034 (10324.300)

2953.886 (12476.930)

3970.873 (13053.120)

4176.923 (10309.380)

4930.946 (14926.280)

tra

0.084*** (0.010)

0.082*** (0.011)

0.084*** (0.010)

0.084*** (0.010)

0.083*** (0.011)

gdpper

0.319 (0.218)

0.250 (0.261)

0.317 (0.224)

0.261 (0.222)

0.224 (0.267)

expegdp

3653.836* (1930.501)

3413.105 (2131.635)

4141.874 (2639.059)

3626.845* (1927.821)

4014.966 (2833.029)

stutea

-173.844 (191.996)

-221.956 (219.792)

-157.649 (201.197)

-169.768 (191.745)

-164.440 (230.666)

enratio

101.893 (262.485)

ppp

25.846 (275.282) -30.718 (48.473)

gdprat R2

0.699

0.706

0.696

-42.009 (50.0460) 12279.923 (10309.380)

14482.520 (10716.950)

0.702

0.700

Note *, **, *** respectively represent the significance at the levels of 10, 5 and 1 %. Same hereinafter

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South Korea

Turkey

Poland

Cuba

Romania

The Czech Republic

China

China

Azerbaijan

Ukraine

Bolivia

Tunisia

Slovakia

India

Vietnam

Turkmenistan

Belarus

Venezuela

Israel

Russia

Syria

Mongolia

Bulgaria

Russia

Ecuador

Ukraine

Ukraine

Lebanon

India

Iran

Kazakhstan

Peru

Bulgaria

Vietnam

Iran

Malaysia

Kazakhstan

China

China

Mauritius

Belarus

Sri Lanka

Uzbekistan

Mongolia

Lithuania

Mexico

Jordan

Poland

Vietnam

Bangladesh

Kyrgyzstan

The Czech Republic

Brazil

India

Kazakhstan

the education quality will count more to the students from developing countries when flowing to peer developing countries. The negative effect of ‘‘higher education enrollment rate of the inflow country’’ may arise from the situation that inflow countries with higher enrollment rates may attach more importance to the education of domestic students, as developing countries generally lack of educational resources and may thus overlook international students to some extent. ‘‘Purchasing power parity in the inflow country’’ is significant at the level of 5 % and in positive correlation, differing from the global integral analysis, which suggests that living expenses outweigh other factors for students from developing countries.

To sum up, the bilateral economic relationship and the gap between developing countries are all in positive correlation with the number of international students, in correspondence with the preliminary assumption of the study. The educational and economic factors are both crucial to students from developing countries when seeking education in their peer developing countries.

Categorized analysis 2: the flow from developing countries to developed countries The top seven developed inflow countries, USA, the Great Britain, France, Australia, Japan, Germany and Italy, with their respective top seven developing outflow countries are selected for this analysis, as shown in Table 5.

According to the results of the empirical study shown in Table 6, ‘‘bilateral trade volume in goods’’ is always significant at the level of 1 % and in positive correlation with number of international students of a country in all the five patterns, congruous with the previous result. Meanwhile, the variable of ‘‘per capita GDP gap between countries’’ is also in significantly positive correlation in all the five patterns. In Pattern 3, the newly added variable of ‘‘purchasing power parity of the inflow country’’ is significant at the level of 5 % and is in negative correlation, coinciding with the expectation and the theoretical conclusion. In Pattern 4, the newly added variable of the ‘‘number of patents approved in the outflow country’’ is significant at the level of 10 % and in positive correlation, as expected. In Pattern 5 where all variables are included, only the variables of ‘‘bilateral trade volume in goods’’ and ‘‘per capita GDP gap between countries’’ are significant.

The above analysis results suggest that the key macro factors influencing the flow of the international students from developing countries to developed countries are the economic

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Pattern 1

Pattern 2

Pattern 3

Pattern 4

Pattern 5

cons

-1268.384** (598.726)

-755.624 (650.750)

515.184** (246.605)

-1255.367* (692.879)

94.197 (295.490)

bitra

1.27E-07*** (1.02E-08)

1.27E-07*** (1.01E-08)

1.11E-07*** (2.86E-09)

1.27E-07*** (1.05E-08)

1.13E-07*** (2.87E-09)

gdpperdif

0.027 (0.030)

0.009 (0.031)

0.033*** (0.011)

0.028 (0.034)

0.069*** (0.018)

expegdp

199.824** (85.716)

63.586 (110.073)

-97.307* (53.343)

200.28* (102.704)

-5.325 (61.849)

stutea

34.538 (25.263)

22.336 (25.897)

9.425 (7.660)

34.228 (26.731)

44.133*** (13.504)

enratio

9.212* (4.705)

-16.725*** (5.387)

ppp

0.070 (0.119)

0.391** (0.003)

tech R2

0.207

0.207

0.393

-0.001 (0.110)

0.000 (0.030)

0.206

0.424

Table 5 The top seven developed inflow countries and their respective top seven developing outflow countries USA

Great Britain

China

China

India

India

South Korea

Malaysia

Mexico

Nigeria

Turkey

Pakistan

France

Australia

Japan

Germany

Italy

Morocco

China

China

China

Albania

Algeria

India

South Korea

Poland

Romania

China

Malaysia

Vietnam

Russia

China

Tunisia

Indonesia

Malaysia

Bulgaria

Cameroon

Senegal

South Korea

Thailand

Turkey

Poland

Thailand

Cyprus

Cameroon

Thailand

Indonesia

Ukraine

Croatia

Saudi Arabia

Poland

Lebanon

Vietnam

Bangladesh

Cameroon

Peru

relations and the GDP gap between the two countries, in perfect match with the reality.

Under certain circumstances, ‘‘purchasing power parity in the inflow country’’ becomes significant and appears in negative correlation. Therefore, the economic factors outweigh all other factors in the flow of students from developing countries to developed countries.

Categorized analysis 3: the flow between developed countries The top seven developed inflow countries, USA, the Great Britain, France, Australia, Japan, Germany and Italy, with their respective top seven developed outflow countries are selected for this part of the study, as shown in Table 7.

According to the results of the empirical analysis illustrated in Table 8, the two variables significant at the level of 1 % and in positive correlation in all patterns are ‘‘bilateral trade volume in goods’’ and ‘‘student- teacher ratio of higher education in the inflow

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High Educ Table 6 The regression results of the flow from developing countries to developed countries Variables

Pattern 1

Pattern 2

Pattern 3

Pattern 4

Pattern 5

cons

-16036.8 (10284.040)

-18609.65* (10408.450)

-14045.190 (10246.010)

-19639.43* (10469.140)

-17518.920 (10891.270)

bitra

2.34E-07*** (1.90E-08)

2.26E-07*** (1.97E-08)

2.22E-07*** (1.96E-08)

2.24E-07*** (1.98E-08)

2.17E-07*** (2.03E-08)

gdpperdif

0.537*** (0.106)

0.467*** (0.116)

0.577*** (0.107)

0.539*** (0.106)

0.536*** (0.123)

expegdp

557.101 (1879.937)

276.790 (1885.238)

440.747 (1866.380)

265.020 (1881.165)

209.950 (1886.379)

stutea

284.049 (293.155)

183.302 (300.402)

357.958 (292.865)

276.311 (292.124)

288.672 (307.773)

enratio

128.369 (87.368)

55.663 (95.973)

ppp

-111.839** (50.947)

tech R2

0.336

0.351

0.289

-78.857 61.628 0.078* (0.047)

0.038 (0.052)

0.331

0.365

Table 7 Top seven developed inflow countries and their respective top seven developed outflow countries USA

GB

France

Australia

Japan

Ireland

Germany

Canada

Canada

Greece

Italy

Japan

Germany

USA

Spain

USA

GB

Germany

USA

France

France

Portugal

Spain

Spain

Italy

Italy

Japan

Germany

Italy

USA

France

Greece

France

Australia

Germany

Germany

Spain

Switzerland

New Zealand

GB

Italy

France

Germany

Australia

USA

Spain

GB

GB

Canada

Greece

USA

Japan

Norway

Sweden

GB

Japan

country’’. The positive impacts of ‘‘student–teacher ratio of higher education in the inflow country’’ suggest that the developed countries have shown aggregation and demonstration effects and thus their transnational education has formed scale economy. This has made these countries a big attraction to students from other developed countries. In Pattern 2, the variable of ‘‘higher education enrollment rate in the inflow country’’ is significant at the level of 10 %, which, to a certain extent, proves a positive effect of the popularization of higher education in the developed inflow countries on the students from other developed countries. In Pattern 5, the variables of ‘‘bilateral trade volume in goods’’, ‘‘student–teacher ratio of higher education of the inflow country’’ and ‘‘higher education enrollment rate in the inflow country’’ are all in positive correlation with the number of international students.

From the above analysis students from developed countries, compared with students from developing countries, rarely value the economic factors when seeking overseas studies in other developed countries, as these developed countries in concern are better developed. Meanwhile, they put more weight on the educational level of the inflow country, the bilateral economic

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High Educ Table 8 The regression results of the flow between developed countries Variables

Pattern 1

Pattern 2

Pattern 3

Pattern 4

Pattern 5

cons

-3433.363 (2157.159)

-4411.553** (2211.371)

-3672.926* (2180.055)

-3372.750 (2199.257)

-4847.766** (2288.270)

bitra

2.91E-08*** (5.86E-08)

2.58E-08*** (6.11E-09)

2.86E-08*** (5.90E-09)

2.93E-08*** (5.98E-09)

2.46E-08*** (6.29E-09)

gdpperdif

-0.029 (0.024)

-0.017 (0.024)

-0.047 (0.033)

-0.028 (0.024)

-0.041 (0.035)

expegdp

598.199 (394.244)

451.699 (400.314)

614.475 (395.084)

605.364 (397.931)

461.368 (400.800)

stutea

281.124*** (57.673)

274.19*** (57.512)

273.301*** (58.562)

280.688*** (57.860)

257.965*** (58.634)

enratio

32.457* (17.602)

ppp

42.321** (19.162) 11.596 (14.698)

tech R2

0.132

0.154

0.081

18.362 (15.750) -0.002 (0.015)

-0.008 (0.016)

0.131

0.062

relations and some other factors that can hardly be quantified, such as the immigration policy, the language barrier and the historic connections etc.

Categorized analysis 4: the flow from developed countries to developing countries The top seven developing inflow countries, Poland, the Czech Republic, Bulgaria, Romania, Turkey, South Korea and Slovakia, and their respective top seven developed outflow countries are selected for this part of the research, as shown in Table 9.

From the results of the empirical analysis in Table 10, the variable of ‘‘student–teacher ratio of higher education of the inflow country’’ is significant and in negative correlation.

This variable shows significance at the level of 5 % in the first four patterns and 1 % in pattern 5. It is worth noting that these impacts differ from those of the flow between developed countries, that a lower ‘‘student–teacher ratio in higher education’’ helps to attract more students from developed countries, as the quality of higher education of developing countries is relatively lower and thus a lower ratio means better quality, which shall help to attract students from developed countries to some extent. In Pattern 2 and Pattern 5, ‘‘per capita GDP gap between countries’’ is also significant at the level of 10 % and in positive correlation, indicating that students from developed countries may choose a less developed country among developing countries when seeking overseas studies. None of other variables has shown a significant characteristic.

Generally speaking, students from developed countries value both educational factors and economic factors when seeking education in developing countries. The improvement of the education quality of developing countries shall help to attract more students from developed countries. The bigger the economic gap, the more attractive the country may be to the students from developed country, thanks to the desire of the students from developed countries to know more about the developing countries, in terms of their culture, lifestyle, language and history etc. In fact, other factors like history relations between developing

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High Educ Table 9 The top seven developing inflow countries and their respective top seven developed outflow countries Poland

The Czech Republic

Bulgaria

Romania

Turkey

South Korea

Slovakia

Norway

GB

Greece

Greece

Greece

Japan

Greece

USA

Portugal

USA

Germany

Germany

USA

Norway

Sweden

Ireland

Germany

Italy

GB

Canada

USA

Germany

Germany

Canada

USA

USA

Germany

Sweden

Canada

Norway

Italy

Hungary

Australia

Australia

Germany

France

USA

Spain

Canada

Austria

New Zealand

GB

GB

Greece

Great Britain

France

France

France

Hungary

Table 10 The regression results of the flow from developed countries to developing countries Variables

Pattern 1

Pattern 2

Pattern 3

Pattern 4

Pattern 5

cons

582.908** (275.449)

617.064** (283.554)

762.201** (347.682)

519.78 (318.641)

1021.175** (457.823)

bitra

7.27E-09 (5.47E-09)

5.93E-09 (6.05E-08)

8.57E-09 (5.68E-09)

6.51E-09 (5.81E-09)

5.20E-09 (6.35E-09)

gdpperdif

0.008 (0.005)

0.009* (0.006

0.008 (0.005)

0.008 (0.005)

0.011* (0.006)

expegdp

80.731 (58.824)

69.624 (62.667)

98.196 (62.371)

79.299 (59.025)

82.363 (63.497)

stutea

-32.680** (13.815)

-37.023** (16.172)

-37.515** (14.957)

-33.124** (13.880)

-57.140*** (21.1350

enratio

2.668 (5.139)

ppp

8.36 (6.466) -1.616 (1.91)

tech R2

0.008

0.006

0.005

-3.489 (2.407) 0.002 (0.006)

0.002 (0.006)

0.003

0.003

countries and developed countries, cultural relations or personal academic interests also can help to attract more students from developed countries.

The robust test Considering the data of the study and the characteristics of the analytical framework of this paper, a robust testing method is adopted. Due to the possibility of the endogenous problems existing between independent variables and dependent variable, which is the reverted causality between the scale of the international talents and the variables, meanwhile owing to the time-lag effect of the independent variables on the scale of the international talents, this paper draws the independent variables with first-order lag for the regression, to test the robustness of the regression results. This method helps overcome the endogenous problems in independent variables, while giving consideration to the time-lag effect of the independent variables on the dependent variables. The regression results can be referred to in Table 11.

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High Educ Table 11 Results of the robust test Perspective

Integral analysis

Categorized analysis 1

Categorized analysis 2

Categorized analysis 3

Categorized analysis 4

Constant

8037 (17041)

-1053 (665.9)

-23423 (15013)

3353 (7092)

-695.6 (1204)

L. gdpper

0.262 (0.332) 0.0918*** (0.031)

0.515** (0.219)

-0.0677 (0.061)

-0.0062 (0.006)

1.74e-07*** (3.11E–09)

1.96e–07*** (6.15E–08)

2.64e–08** (1.07E–08)

1.03e–08** (3.87E–09)

L. gdpperdif L.tra

0.0669*** (0.0125)

L.bitra L.gdprat

93.99 (116.300)

L.enratio

-155.6 (312.9)

–2.59 (14.5)

61.23 (164.8)

24.86 (77.3)

24.26 (18.9)

L.expegdp

7350** (3514)

-79.34 (110.7)

2708 (2155)

–124.9 (744)

25.51 (43.34)

L.stutea

-331.8 (247.1)

44.01** (21.5)

278.2 (303.0)

51.59 (87.1)

–45.04 (37.2)

L.ppp

-58.82 (90.6)

0.581*** (0.20)

-106.4 (128.2)

9.772 (25.0)

-1.572 (2.1)

0.00369 (0.005)

-0.00554 (0.058)

-0.0124 (0.045)

0.00511 (0.007)

0.954

0.444

0.105

0.107

L. tech R2

0.245

From Table 11 five conclusions may be drawn. (1) From each of the five perspectives, the regression coefficient of the trade volume in goods is positive and significant, indicating that commodity trade is a key determinant of the international talent flow, consistent with the results of previous empirical analysis. (2) According to the analysis of the flow between developing countries, the regression coefficients of ‘‘bilateral trade volume in goods’’, ‘‘per capita GDP gap between countries’’, ‘‘student–teacher ratio of higher education in the inflow country’’ and ‘‘purchasing power parity in the inflow country’’ are all positive and significant. As for the flow from developing countries to developed countries, ‘‘per capita GDP gap between countries’’ and ‘‘bilateral trade volume in goods’’ are all positive and significant. These results also match with the previous empirical study. (3) From the analysis of developed countries, the only positive and significant regression coefficient is ‘‘bilateral trade volume in goods’’, with all other factors on the contrary. (4) From the global perspective, the regression coefficients of ‘‘trade volume in goods’’ and ‘‘higher education expenditure as percentage of GDP’’ are positive and significant, fitting with the previous empirical analysis as well. In a word, the test proves that the results from the empirical analysis are robust.

Conclusions The empirical analysis of international talent flow brings the following conclusions: 1. From four of the five perspectives, the number of international students (the scale of service trade of higher education) in the inflow country has a positive correlation with

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High Educ

its trade volume in goods, with the flow from developed countries to developing countries as an exception. As transnational education falls within the scope of trade, and with commodity trade both are representatives of the economic relationship between one country and another. The larger volume of commodity trade may indicate a stronger bilateral economic relationship, so the bilateral commodity trade can facilitate the flow of international students.

2. In the case of students from developing countries, they, compared with students from developed countries, put more weight on the economic factors, regardless of the inflow country being a developed or developing one. Among developed countries, they are more inclined to choose the better developed one, and thus ‘‘per capita GDP gap between countries’’ is a key determinant in this sense. Among developing countries, they value the economic factors as well as other factors, such as ‘‘higher education expenditure as percentage of GDP’’, ‘‘per capita GDP gap between countries’’, and ‘‘purchasing power parity in the inflow country’’.

3. In the case of students from developed countries, the educational factors rather than the economic factors rank on the top of the list, such as ‘‘student–teacher ratio of higher education in the inflow country’’ and ‘‘higher education enrollment rate in the inflow country’’, when making a choice among other developed countries as potential destinations. As for developing countries as potential destinations, they put the same weight on both the economic and educational factors, thus ‘‘student–teacher ratio of higher education in the inflow country’’ and ‘‘per capita GDP gap between countries’’ are the key determinants.

4. Therefore, the growth in the faculty scale and expenditure in higher education are both of crucial importance for developing countries to attract international talents. From the results of the integral regression analysis, ‘‘higher education expenditure percentage of GDP’’ is one of the most important determinants. And ‘‘higher education expenditure as percentage of GDP’’ is of the same weight in the flow between developing countries, while ‘‘student–teacher ratio of higher education in the inflow country’’ plays a key part in the flow from developed countries to developing countries.

It is certain that international students are just one type of talent and the influencing factors of the talent flow vary greatly due to the various types of talents and countries.

Therefore, policies should be tailored to the characteristics of the talent flow.

In summary, international students as one type of talent help retain the competitiveness of the domestic organizations and play a key role in the future economic development of the inflow country, if international students stay and work in the inflow country after finishing their study. Compared with the talent directly attained from abroad, the international students with a local educational background have more advantages as their younger age, fluency in the language and familiarity with the job market shall all enable them to adapt and suit better to the local environment and enterprises (Hawthorne 2008).

Meanwhile, international students are very familiar with their homeland, which will help to improve the relationship between the inflow country and their home country. Furthermore, the diversity of talents is good for innovation, and the inflow of international students has positive effects on knowledge flows. There is a close relationship between international student flow and other types of talent flows, because international students may tell others about their experiences and may have networks that other types of talents may use (Dreher and Poutvaara 2005).

It is no denying that international student flow is an efficient way of attracting future needed talents, and countries aiming to attract talents from other countries can benefit from

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offering education for international students. So countries aiming to attract talents from other countries should pay more attention to attract international students and encourage them to seek career in local job markets after finishing study.

Acknowledgments This paper is supported by ‘‘Research Fund of Beijing Planning Office of Philosophy and Social Science’’ (NO.10BaJG334), ‘‘The Scientific Research Fund of Education Ministry of China’’ (No. 10YJC790272), ‘‘The Scientific Research Fund of National Planning Office of Philosophy and Social Science’’ (No.10zd&017, No.11AJL005, No.11FJL008), ‘‘The Fundamental Research Funds for the Central University’’ (NO. 105563GK), and ‘‘The Fundamental Research Funds for 985 Projects’’. I would like to express my great appreciation to three anonymous reviewers of Higher Education for their valuable comments on this manuscript as it developed.

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