ABSTRACT
ABSTRACT
This paper examines how country-specific factors in receiving countries influence a highly skilled migrant’s choice between several possible locations. While continental European countries recognize that attracting migrants is a key component of their economic strategies, it is unclear to what extent these immigration policies result in European countries performing better in the global competition for the skilled. Surveys of prospective migrants in India show that while European countries appear to be relatively attractive for educational purposes, European countries are not perceived as favourably for long-term stays. Relative to migrants selecting traditional immigration countries, migrants selecting Europe as a destination typically have more skills and increased access to resources, such as existing networks abroad, higher educational level or better language skills. With fewer long-term migration initiatives to Europe, immigration policies and destination country-specific factors, opportunities to obtain citizenship and amenities of local environment become less relevant. European governments put considerable effort in integrating student migration as a part of a wider immigration strategy; however, this strategy is likely to prove ineffective if ‘probationary migrants’ do not view European countries as realistic work destinations after graduation.
Introduction
There is growing agreement that international student mobility is a particularly valuable channel of highly skilled immigration, because the international students graduating from a host country are already well-adapted to that host country’s labour market. Studies show that students who studied abroad are more likely to work abroad after the completion of their studies relative to other domiciled students (De Grip, Fourage, and Sauermann 2009; Findlay et al. 2005; King, Ruiz-Gelices, and Findlay 2004; Tremblay 2002; Wiers-Jenssen 2008). Student migration is considered a form of knowledge migration by industrialized countries, which are changing their policies to become more attractive to students and highly skilled migrants. European migration policies have become increasingly favourable towards the admission of highly skilled workers. These new policies include advantageous rules specifically for young migrants and former students, who are received in host countries as ‘probationary migrants’ (Millar and Salt 2007). Given the recent changes in immigration and higher education policies combined with the general consensus that highly skilled immigration is desirable for Europe (Kahanec and Zimmermann 2011), we have a special interest in assessing how potential skilled migrants perceive Europe.
The study focuses on pre-departure students in India. Highly skilled Indians concentrate in a few industrialized destination countries. Around 80% of all Indian migrants with tertiary education reside in only three countries: the United States, the United Kingdom and Canada. The United States alone hosts 66% of all tertiary-educated Indian migrants (OECD 2015). It is also the main destination for Indian students, attracting 45.7% of all Indians who studied abroad in 2011. While the United States, United Kingdom and Australia attract 83.5% of Indian students enrolled abroad, we also observe upsurges in enrolments in other destinations such as Canada and New Zealand, and continental European countries like Germany and France (OECD 2013).
In order to determine whether continental European countries have successfully entered the global competition for talent, this paper compares the perceptions potential migrants have about these European countries relative to the views they have about traditional immigration countries. The paper uses survey data from science and engineering students at Indian universities to assess how potential migrants make decisions regarding potentially moving abroad. We first present previous research on how student choices affect international mobility. Second, we analyse the survey data and observe if students differ when compared by preferred destination country in any of the personal and background factors as well as in their expectancy-based perceptions. The paper concludes with policy proposals that would more effectively incentivize migration to certain countries.
Related literature
Increased student mobility has not gone unnoticed within academic research. The majority of studies focus on the demand side, observing educational institutions and suggesting improvements to the offers made by universities to international students (Binsardi and Ekwulugo 2003; Mazzarol 1998). The early work on student mobility considers ‘university offerings’ not only in terms of core educational services, but also with regard to ancillary offerings of tangible and intangible attributes (Grönroos 1978, 1994; Levitt 1980). Few authors (Binsardi and Ekwulugo 2003; Bourke 2000; Kolster 2014; Srikatanyoo and Gnoth 2002) focus on the factors unrelated to education (such as the country characteristics, country image effect and students perceptions of potential locations) on the decision-making in international tertiary education. In these studies, country image is said to directly influence students’ attitudes towards the country’s academic institutions, but no clear conclusions can be drawn. Kolster (2014) suggests that it is important to study perceptions of international students in addition to factual country characteristics. Among the few papers explaining the difference in migration decisions with respect to geographic areas, De Grip, Fourage, and Sauermann (2009) and Constant and D’Agosto (2008) look into the determinants of country choice for European science and engineering graduates and for Italian scientists and researchers, respectively. De Grip et al. find that among European science and engineering graduates, wages matter only for migration within the European Union but not for migration to Anglo-Saxon countries, which attract people for higher R&D intensity. Differences between graduates’ destinations are found with respect to disciplines, previous migration experiences of graduates and migration experiences of parents. The findings by Constant et al. show that gender, education, international work experiences, the field of specialization, motives for migration and duration of residence abroad can also predict the country choice for Italian scientists and researchers.
Of particular relevance to this research, Mahmood and Schömann (2003) look at the factors influencing selection of alternative countries for Information Technology graduates in India and compare these factors with the option of staying in India. When comparing foreign destinations, results indicate that the United States and Canada have an advantage over Germany in the areas of self-employment, high-career positions, social networks and residence permits. Economic aspects are said to be more important for migration decisions than institutional and socio-political factors.
Data
The data for this study were collected during two field visits to India. Data collection took place in March and April 2009 among students at Jawaharlal Nehru University (JNU), Institute of Technology – Banaras Hindu University (IT-BHU), and University of Jammu. In August 2009, the data were collected at the Indian Institute of Technology (IIT) Delhi and Indian Institute of Science (IISc) Bangalore. All chosen institutions have reputations for providing high-quality education, as either recognized by the University Grants Commission (UGC) or graded by The National Assessment and Accreditation Council (NAAC) under the ‘A category’ denoting ‘High level of academic accomplishment as expected from an institution’ (NAAC 2007). All survey participants were Indian students studying science and/or engineering at the selected institutions. In total, 412 students participated in the survey, answering sets of questions on their personal situations, preferences to move abroad and social networks.
Table A1 in the Appendix illustrates the respondents’ personal characteristics, university and family background, migration history and social network abroad. Consistent with expectations, a large share of survey respondents stated that they are considering moving abroad in the future (63.6%). The analysis presented in this paper looks only at those students who have expressed intentions to move abroad. Behavioural intentions are considered good predictors of actions, if they measure somewhat specific behaviour in a restricted time span in which an individual has significant freedom of choice (Van Dalen and Henkens 2008). The question addressed to the respondents is therefore purposely specific about their future plan regarding location choice within an exact time frame. Respondents were asked to name only one country as their first choice if they were to move abroad in the next five years. Similar to the general distribution of Indian students abroad, we find a dominance of preference for going to the United States (52.7%). The other frequently mentioned countries are Germany, the United Kingdom, Australia and Canada. As expected from the general patterns of Indian skilled migration, preferences for a destination country from our sample clearly show that some countries have an obvious advantage in attracting the population of skilled Indians. In our sample, however, continental European countries are frequently mentioned as a first preference. Interestingly, Germany is picked more often than the United Kingdom or Australia, which in overall trends of Indian international student mobility are second and third, respectively, after the United States. Other continental European countries, namely Switzerland, The Netherlands and France, in order of frequency, are also often selected as a first option. This outcome offers a positive indication with respect to the competitiveness of European countries for skilled migrants. The next section more closely analyses respondents selecting Europe as their preferred foreign destination. We observe whether there are any apparent differences between those students who choose European countries relative to those who choose any of the Anglo-Saxon countries.
Comparison of respondents by preferred destination
In order to determine whether respondents differ in personal and other background factors by destination country, we divide respondents into three categories by preferred destination: (1) the United States, (2) other Anglo-Saxon countries and (3) continental European countries. Migration choices are sorted into the three mentioned groups according to relevant criteria determining migration patterns. The United States’ universities and high technology companies have worked as a magnet for Indians for decades, leading to a strong migration network. A vast majority of skilled Indians are exclusively interested in migrating to the United States. The other Anglo-Saxon countries, namely the United Kingdom, Canada, Australia and New Zealand, have been historically open to immigrants; they are all English-speaking countries, and are linked to India via a colonial history. The four named Anglo-Saxon countries all have a supply-driven immigration policy for the highly skilled, where applicants for skilled migration are selected based on their attributes and capabilities. The European continental countries, the third group, differ from all the Anglo-Saxon countries in several aspects. The fact that English is not the main language spoken in these countries leads to a language barrier, which might make these countries less attractive for English-speaking students. These countries also lack historical traditional links with India and migrant networks which could facilitate migration and inspire potential migrants.
In Table A2 in the Appendix1 we observe how the various factors of respondents’ interest differ by preferred countries. We use the Pearson’s Chi-square test to test whether people with different personal and structural background characteristics also differ in frequency with which they express preferences for migration destinations. We further review if any of the migrant dimensions has an effect on choosing a particular group of countries. To find the level of statistical significance linked with a single cell value, we conduct a residual analysis.
Students’ personal profiles do not differ significantly between those choosing one destination over another. Only in terms of having children, level of studies and having networks abroad are there statistically significant differences in country choice. Students with children are significantly more likely to choose European destinations. When looking at students’ community belonging, we observe that among those students choosing the United States, there are relatively more Hindus than in the other two destination groups. In the past decades, the United States has been the main destination for highly skilled Indians and has an established Indian community consisting primarily of Hindus. While Hindus are the dominant emigrant group in all destinations, the United States stands out with the highest relative percentage of Hindu migrants (Kapur 2010). As the network effect pulls future flows towards the existing migrant population of their own community, this could explain relative preference of Hindus to follow in the footsteps of other Hindus. At the same time the networks of previous migrants from a non-Hindu community are scarce and are hence missing the positive effects which could be available in the form of providing relevant information and facilitating access to universities and future employment.
In terms of the university background, we observe that students in different levels of their study programmes differ in terms of the chosen destination country. Among those students who pick European countries, there is a higher than average representation of students in their Bachelor programmes. The Anglo-Saxon countries attract more masters students, and respondents pursuing Ph.D. studies or post-doctoral studies have low representation among those who picked any of the four Anglo-Saxon countries. Looking closer into the differences between country choice, we observe that continental European countries have greater attraction for people in natural sciences (as opposed to engineering fields). In terms of students’ performance, only minor differences exist between the chosen countries. Proficiency in English displays unexpected outcomes. It is difficult to explain with the results of this study why there is a higher proportion of respondents with a good knowledge of English for the continental European countries.
We observe that among students who have a preference for continental European countries a significantly higher percentage has lived abroad in the past as compared to students preferring other destinations. Relatedly, it is those respondents who have a preference for Europe that are more likely to have friends and colleagues living abroad. Judging from this result, we theorize that social ties are more important for planning emigrate to new destination countries as opposed to emigrating to dominant destinations where the majority of migrants had gone.
Evaluation of factors
Survey respondents were asked to rank on a five-point scale the importance they place on a number of stated factors for the country to which they want to move. We divide the responses by preference for destination country/region into three groups. Table A3 and A4 provide the mean values of all factors for each of the three studied destination regions.
It is observed that students wanting to go to the United States value the majority of factors higher than students preferring the other two observed destinations. The most important factors for all students are related to their career path. The students who have picked the United States as their preferred country choice place, on average, the highest value on quality and content of their work, good research facilities and recognition of qualifications. Also for the students preferring other Anglo-Saxon countries, quality of work is considered most important. For continental European countries, this factor came only as the eleventh most important in mean values. The most important for respondents with preferences for continental European countries is good quality of higher education institutions, followed by quality of research facilities, which clearly shows their focus on moving abroad for the purpose of studying. Interestingly, students preferring Anglo-Saxon countries place high importance on safety factors. These students value public safety as well as political stability among their five most important factors.
Given that students who prefer the United States in general rank factors with greater importance, it is difficult to compare the mean values across countries/regions. The biggest differences in mean values between the group preferring the United States and the group preferring the European countries is with regard to quality and content of work, the need to learn a new language and in the value given to the possibility of not working more than eight-hour days. Students who prefer European countries place a higher mean value in comparison with those preferring the United States only in three cases: rich cultural institutions, social security and benefits and friendly hospitable population. However, considering that students preferring Europe ranked these factors relatively low in terms of importance means they do not have much value in terms of attractiveness. In a separate question we ask students the importance of living near a large Indian community in a prospective host country. As there are still relatively small numbers of migrants from India in continental European countries, as expected, migrant networks are not relevant to those who prefer moving to Europe over other destinations.
In addition, students were asked to rank the importance of policy-related factors (Table A4). It is notable to observe that in terms of immigration policies, the possibility of permanent settlement and acquisition of citizenships rank the lowest. Especially for students who choose Europe as a destination area, the possibility of settlement is particularly unimportant. These students are much more interested in clear application procedures and the chance that immigration policies will allow them to re-enter the country later in life.
We have so far demonstrated that there are characteristic differences between people preferring different locations for migration. When categorized by preferred location choice, students also weight different factors relevant for the potential host country. To identify which of these factors increases the likelihood that respondents express plans to go to a specific location, we use a multinomial logit regression analysis as described below.
The multinomial logit regression
We use a multinomial logit specification to model the choice between three prospective destinations: (a) the United States, (b) the Anglo-Saxon countries and (c) continental European countries. Multinomial logit models are used to model relationships between a polytomous response variable and a set of regressor variables (Kuhfeld and So 2007). This specification testing of whether the factors associated with preferring one destination is statistically different from the factors associated with preferring another destination. The multinomial logit model is based on the principle that individuals choose the outcomes which maximize the utility derived from their choice. An individual i (i = 1, … , N) faces m possible choices, with 
denoting the level of indirect utility associated with the jth choice, called the latent variable. The observed variables Yij are defined as:
If
where Xj is the vector of attributes for the jth choice and εj is the random error associated with that choice, the specific form of the model is determined by the assumed distribution of ε and the specification of how Vj(Xj) is related to the measured variables (Constant and D’Agosto 2008).
The dependent variable is a categorical variable of three unordered outcomes. In estimating the model, the continental European countries are chosen as the reference outcome to which we compare the remaining two alternative destinations. The explanatory variables should explain the impact of demographic factors, university background as well as the value placed on economic, socio-political and institutional factors on country choices. On the basis of the earlier empirical studies as well as based on the observations from the descriptive statistics, we select a group of variables which could explain the country choice of individual students. Table A5 (in the Appendix) reports the results of the multinomial logit model with the reference category of having intentions to move to the continental European countries. In this section, we focus on the statistically significant results only.
The results show that people in relationships are less likely to choose the United States. In fact, the marginal effects show that they are 39% less likely to plan the move to the United States than single people, compared to the option of going to European countries. Also the field of studies turns out to be a significant determinant for location choice. Those who study engineering are more likely to go to the United States compared to students in natural sciences. The results clearly show that country choice can be explained by level of educational programme. Compared to students enrolled in Bachelor programmes, both Masters students, Ph.D. students and post-doctoral researchers are more likely to move to the United States. This result is particularly strong for Ph.D. students and post-doctoral researchers.
We further show that parental support is more important for going to Europe. For both alternative destinations, students perceive their parents’ attitude towards their move abroad as less encouraging than students planning to go to Europe. In addition to having parental support, the networks of people living abroad also help explain country preferences. As migration network theory focuses on the prediction of new migration flows based on the settlement of migrants in specific places of destination (Stark and Wang 2002), we find that having friends who live abroad plays a significant role for choosing the continental European countries versus the United States. Our results show that so-called weak ties in contacts with friends and colleagues matter more than ‘strong ties’ in family network, congruent with Granovetter’s hypothesis on the ‘strength of weak ties’ (1973).
In our estimation, we also included the rating of factors of importance for respondents’ consideration on the location choice, limited to those factors, which were found to be relatively more important by the respondents, and those factors where we found larger differences between students preferring different destinations. Looking at subjective ratings of importance in our results, the country choice decision is influenced by the importance placed on the quality of educational institutions. Students who consider high-level educational institutions very important are less likely to pick the Anglo-Saxon countries. In comparison, students choosing the continental European countries value educational factors very highly, indicating that continental Europe is primarily a destination for studying and to a lower extent considered as a location for a longer stretch of time.
The local characteristics, also referred to as amenities, affect quality of life because people have preferences for certain types of areas, such as neighbourhoods that offer more security, better access to facilities, and the like. Our results show that the importance placed on amenities of local environment does not vary significantly between Indian students choosing different locations. The feeling of safety, importance of living in a family-friendly environment and being in a different language environment do not determine preferences for locations. Still, having only minor Indian communities in continental European countries cuts against choosing any of these countries as a potential destination for students who highly value the closeness of Indian community. Students preferring to live close to an existing Indian community exhibit a clear, statistically significant, preference for the United States.
In order to observe the effect of immigration policies in migration decisions, we include the respondents’ rating of how important application procedures are for their prospective host country. Our results show that such ratings are significant determinants for the choice of going to the United States compared to migrating to continental European countries. This supports the assertion that migrants preferring continental European countries do so primarily for purposes of higher education, reducing the relevance students place on admission procedures. Considering that admission procedures are less demanding for international students, those respondents who are exclusively interested in going abroad for studying are found to place less importance on immigration procedures.
Conclusion and discussion
Despite American fear that the United States is losing its status as the preeminent destination for the best and the brightest from all over the world (Wadhwa 2012), our results indicate this fear is misguided. For Indian prospective migrants the United States remains the prime choice despite the efforts of other industrialized countries to attract talents and skills from abroad.
The four-decade long history of ever increasing waves of highly skilled migration between India and the United States resulted in ‘geographical structuring and clustering of migration flows’ (Bakewell, de Haas, and Kubal 2011, 5), making emigration to the United States nearly synonymous with the decision of going abroad. The movements of large flows of skilled Indians to the United States is linked also with large flows of goods, capital, ideas and information (Fawcett 1989), which leads to ‘an identifiable geographical structure that persists across space and time’ (Mabogunje 1970, 12). Most migrants follow an informational cascade and herd behaviour in trusting the decisions of preceding migrants, which leads to difficulties for new destinations, such as continental European countries, in attracting foreign talent. Our findings show that European countries are relatively attractive destinations for Indian students for educational purposes but have difficulty in attracting Indian migrants permanently. Continental European countries are not perceived as having as promising career opportunities for long-term stays. Since few students planning to go to continental Europe plan to stay longer than five years, the possibility of settlement or obtaining citizenship is perceived as unimportant for their move abroad. As such, their decision to pick Europe can be explained by prioritizing quality of educational institutions. These findings illustrate the problems facing continental European countries in retaining foreign students in the local labour market. That students who chose the United States or the other Anglo-Saxon countries place the highest value on quality and content of their work, while those selecting continental European countries considered this unimportant, illustrates that European countries must change both the perception of and actual career possibilities in order to be regarded as attractive career destinations.
We find that students choosing European countries differ in several aspects from those choosing the United States. The most salient finding is that respondents preferring European destinations possess more resources and skills; be it in terms of existing networks abroad or better language skills. These additional resources and skills enable potential movers to overcome the lack of information, and related higher costs and risks entailed by migration to less common destinations (De Haas 2010, 12). On the contrary, migration to the United States and to other Anglo-Saxon countries appears not to require first-hand information from personal contacts since there is abundant information available from previous migration, emerging into a migration system (Mabogunje 1970). Information about migrants’ success and reception in receiving countries is conveyed back to the origin country through news as well as through other widely available feedback mechanisms, which reduces unobserved conditions in receiving countries (Radu 2008). In this case existing migrant network is not required, as most migrants will move where the others have gone earlier, leading to self-reinforced migration behaviour (Epstein 2008). For new destinations, however, networks are important for sharing information and increasing awareness and aspirations for considering that specific place. Therefore, students who would pick any of the European countries use their friends abroad as ‘bridgeheads’ (Böcker 1994) by depending on this bridging capital to provide information on existing options. It is the access to information which makes migration to new destinations more likely for groups with strong bridging capital (De Haas 2010).
Adjustments of migration policies in Europe that enable students to stay in destination countries after completion of their studies have not activated the desired response to render these locations more attractive for work migration. The competition for the best and the brightest of the world is strong and is a relevant policy concern of many governments. Despite the efforts of many European countries to integrate student migration as a part of a wider immigration strategy, Europe may continue to be the ‘land of missed opportunity, unable to attract the talent’ (Boeri et al. 2012, 1), if ‘probationary migrants’ do not view these European countries as prospective work destinations after graduation. The perception of Europe as a short-term destination reduces the relevance of several favourable options introduced into immigration policies to attract skilled migrants. Our results as well as other empirical studies (Binsardi and Ekwulugo 2003; Boeri et al. 2012; Constant and D’Agosto 2008; De Grip, Fourage, and Sauermann 2009; Soutar and Turner 2002) show that career prospects matter most to attract the highly skilled, which provides clear policy implications. Improving access to the labour market for foreign workers and providing a more seamless transition from studying to the local labour market would provide the missing link in placing continental Europe more visibly on the map of the global race for talent. Given that economic factors override institutional and political factors in migration decisions, it is most important for European countries to change the perception of career opportunities, in addition to the overhauling immigration policy. Although this analysis focused on an Indian case study, the mobility patterns shown in this research depict a persistent dominance of one traditional destination country and illustrate how migration flows are shaped by factors beyond migration policies. Governments can certainly still shape institutions and processes that provide links to potential migrants. By fostering exchange between countries, social networks can be created to counteract previously limited historical ties. However, this is not a process that can be changed within a short time frame.
| Values | % | |
|---|---|---|
| Personal characteristics | ||
| Gender N = 327 | Female | 29.05 |
| Male | 70.95 | |
| Age N = 318 | 22 or younger | 39.62 |
| Between 23 and 26 | 35.22 | |
| 27 and older | 25.16 | |
| Community N = 302 | Non-Hindu communities | 20.20 |
| Hindus | 79.80 | |
| Reserved group N = 310 | Reserved group | 16.45 |
| Non-reserved group | 83.55 | |
| Relationship N = 320 | Single | 76.88 |
| Relationship (boyfriend/girlfriend) | 13.13 | |
| Married | 10.00 | |
| Children N = 321 | No children | 75.39 |
| Children | 24.61 | |
| University characteristics | ||
| University N = 350 | JNU | 41.43 |
| IISc Bangalore | 21.14 | |
| IIT Delhi | 12.86 | |
| BHU-IT | 12.00 | |
| Jammu | 12.57 | |
| Field of studies N = 314 | Natural sciences | 34.71 |
| Engineering | 65.29 | |
| Detailed fields of studies N = 314 | Computer and systems sciences | 13.69 |
| Information Technology | 7.32 | |
| Physics | 6.05 | |
| Math | 5.41 | |
| Life sciences | 20.38 | |
| Bio technology | 9.87 | |
| Environmental sciences | 5.73 | |
| Engineering | 20.70 | |
| Food science | 7.96 | |
| Chemistry | 2.87 | |
| Level of studies N = 305 | Bachelor programmes | 26.89 |
| Masters programmes | 35.08 | |
| Ph.D. and Post-Doc | 38.03 | |
| Average grade N = 293 | Second (B+, B, B−) and third class (below C+) | 26.96 |
| First class (A+, A, A−) | 73.04 | |
| Proficiency in English N = 317 | Medium or lower (3–5) | 24.61 |
| Very good or good (1–2) | 75.39 | |
| Family background | ||
| Mother’s highest education level N = 315 | None, or some primary | 6.98 |
| Completed primary | 5.71 | |
| Secondary | 28.89 | |
| Vocational | 5.71 | |
| University | 52.70 | |
| Father’s education N = 316 | None, or some primary | 2.22 |
| Completed primary | 2.85 | |
| Secondary | 13.29 | |
| Vocational | 7.59 | |
| University | 74.05 | |
| Support of family to move abroad N = 317 | Encourages move | 58.68 |
| Prefers stay | 35.33 | |
| Doesn’t care/neutral | 5.99 | |
| Average monthly income of the household N = 314 | Less than Rs. 25,000 per month | 40.45 |
| Between Rs. 25,001 and 30,000 per month | 18.79 | |
| Between Rs. 30,001 and 40,000 per month | 16.24 | |
| More than Rs. 40,000/- per month | 24.52 | |
| Area of parents’ residence N = 319 | Urban metropolitan area | 32.29 |
| Semi-urban, smaller cities and towns | 52.04 | |
| Rural area | 15.67 | |
| Migration history | ||
| Lived abroad N = 371 | Not lived abroad | 86.25 |
| Lived abroad | 13.75 | |
| Network abroad | ||
| Parents lived abroad N = 289 | Not lived abroad | 91.70 |
| Lived abroad | 8.30 | |
| Brother or sisters lived abroad N = 289 | Not lived abroad | 82.35 |
| Lived abroad | 17.65 | |
| Extended family abroad N = 293 | Not lived abroad | 57.34 |
| Lived abroad | 42.66 | |
| Friends abroad N = 289 | Not lived abroad | 48.79 |
| Lived abroad | 51.21 | |
| Colleagues abroad N = 285 | Not lived abroad | 59.30 |
| Lived abroad | 40.70 | |
| USA | Anglo-Saxon | European | Total | ||
|---|---|---|---|---|---|
| Total | 138 | 52 | 56 | 246 | |
| N = 246 | 56.10 | 21.14 | 22.76 | 100% | |
| Personal characteristics | |||||
| Gender N = 193 Pr = 0.579 | Male | 72.55 | 73.33 | 80.43 | 74.61 |
| Female | 27.45 | 26.67 | 19.57 | 25.39 | |
| Age N = 184 Pr = 0.409 | 22 and younger | 26.80 | 36.59 | 21.74 | 27.72 |
| Between 23 and 26 | 35.05 | 39.02 | 43.48 | 38.04 | |
| 27 and older | 38.14 | 24.39 | 34.78 | 34.24 | |
| Community N = 173 Pr = 0.149 | Non-Hindu | 20.65* | 35.90 | 30.95 | 26.59 |
| Hindu | 79.35* | 64.10 | 69.05 | 73.41 | |
| Reserved status N = 183 Pr = 0.367 | Reserved | 16.33 | 11.90 | 23.26 | 16.94 |
| Non-reserved | 83.67 | 88.10 | 76.74 | 83.06 | |
| Relationship N = 186 Pr = 0.483 | Single | 79.00 | 69.77 | 74.42 | 75.81 |
| Married/in a relationship | 21.00 | 30.23 | 25.58 | 24.19 | |
| Children*** N = 186 Pr = 0.007 | No children | 72.73 | 93.02*** | 65.91* | 75.81 |
| Children | 27.27 | 6.98*** | 34.09* | 24.19 | |
| University characteristics | |||||
| Type of university N = 210 Pr = 0.833 | Research-oriented | 84.07 | 87.50 | 83.67 | 84.76 |
| Practical/applied | 15.93 | 12.50 | 16.33 | 15.24 | |
| Study field N = 188 Pr = 0.322 | Natural sciences | 36.73 | 35.56 | 48.89 | 39.36 |
| Engineering | 63.27 | 64.44 | 51.11 | 60.64 | |
| Level of studies** N = 181 Pr = 0.021 | Bachelor | 18.95 | 14.29 | 29.55* | 20.44 |
| Masters | 29.47 | 50.00*** | 18.18*** | 31.49 | |
| Ph.D. and Post-doc | 51.58 | 35.71* | 52.27 | 48.07 | |
| Average grade N = 171 Pr = 0.539 | Lower than first class (below B+) | 25.84 | 17.07 | 24.39 | 23.39 |
| First class (A+, A, A−) | 74.16 | 82.93 | 75.61 | 76.61 | |
| Proficiency in English N = 183 Pr = 0.209 | Medium, Bad, Very bad | 20.83 | 20.93 | 9.09* | 18.03 |
| Very good and Good | 79.17 | 79.07 | 90.91* | 81.97 | |
| Family background | |||||
| Mother’s highest education N = 183 Pr = 0.533 | Less than university education | 47.92 | 58.14 | 50.00 | 50.82 |
| University education | 52.08 | 41.86 | 50.00 | 49.18 | |
| Father’s highest education N = 184 Pr = 0.760 | Less than university education | 25.77 | 30.95 | 24.44 | 26.63 |
| University education | 74.23 | 69.05 | 75.56 | 73.37 | |
| Support of family to move abroad N = 183 Pr = 0.612 | Encourages move | 63.92 | 75.61 | 66.67 | 67.21 |
| Doesn’t care/neutral | 7.22 | 2.44 | 8.89 | 6.56 | |
| Prefers stay | 28.87 | 21.95 | 24.44 | 26.23 | |
| Average monthly income of the household N = 181 Pr = 0.539 | Less than Rs. 25,000 | 45.36 | 31.71 | 41.86 | 41.44 |
| Between Rs. 25,001and 30,000 | 15.46 | 29.27* | 16.28 | 18.78 | |
| Between Rs. 30,001 and 40,000 | 13.40 | 14.63 | 18.60 | 14.92 | |
| More than Rs. 40,000 | 25.77 | 24.39 | 23.86 | 24.86 | |
| Area of residence N = 185 Pr = 0.257 | Urban metropolitan area | 27.27 | 23.81 | 43.18** | 30.27 |
| Semi-urban, smaller cities and towns | 56.57 | 54.76 | 40.91* | 52.43 | |
| Rural area | 16.16 | 21.43 | 15.91 | 17.30 | |
| Migration history | |||||
| N = 221 Pr = 0.213 | Respondent not lived abroad | 86.99 | 86.96 | 76.92* | 84.62 |
| Lived abroad | 13.01 | 13.04 | 23.08* | 15.38 | |
| Network abroad | |||||
| Parents N = 167 Pr = 0.403 | Parents not lived abroad | 93.26 | 86.11 | 92.86 | 91.62 |
| Lived abroad | 6.74 | 13.89 | 7.14 | 8.38 | |
| Sibling* N = 167 Pr = 0.078 | Siblings not lived abroad | 82.95 | 73.68* | 92.68* | 83.23 |
| Lived abroad | 17.05 | 26.32* | 7.32* | 16.77 | |
| Extended family N = 167 Pr = 0.950 | Extended family not lived abroad | 57.95 | 55.26 | 58.54 | 57.49 |
| Lived abroad | 42.05 | 44.74 | 41.46 | 42.51 | |
| Friends N = 169 Pr = 0.142 | Friends not lived abroad | 45.56 | 43.59 | 27.50** | 40.83 |
| Lived abroad | 54.44 | 56.41 | 72.50** | 59.17 | |
| Colleagues*** N = 165 Pr = 0.002 | Colleagues not lived abroad | 54.02* | 59.46 | 24.39*** | 47.88 |
| Lived abroad | 45.98* | 40.54 | 75.61*** | 52.12 | |
Note: Pearson’s Chi-square test; significance levels *p < .1, **p < .05, ***p < .01.
| USA | Anglo-Saxon | European | Total | |
|---|---|---|---|---|
| High demand for my qualifications | 4.432 | 4.372 | 4.222 | 4.366 |
| Easily finding a suitable job after my studies | 4.168 | 4.093 | 4.089 | 4.131 |
| Attractive salary | 4.365 | 4.214 | 4.067 | 4.257 |
| Quality and content of my work | 4.751 | 4.5811 | 4.289 | 4.5981 |
| Good research facilities in companies and public institutions | 4.6912 | 4.4423 | 4.5112 | 4.5892 |
| No more than 8-hour working days | 3.674 | 3.571 | 3.356 | 3.571 |
| Career progression opportunities | 4.5215 | 4.286 | 4.43 | 4.4375 |
| Recognition of educational/professional qualifications | 4.6843 | 4.317 | 4.3645 | 4.5224 |
| Job security (not easy for employers to fire workers) | 4 | 4.000 | 3.909 | 3.978 |
| Costs of living | 4.084 | 3.929 | 3.954 | 4.017 |
| Family-friendly environment | 4.032 | 4.4194 | 3.977 | 4.11 |
| Good quality of higher education institutions | 4.6294 | 4.372 | 4.5331 | 4.5463 |
| Multicultural environment | 4.011 | 3.927 | 3.909 | 3.967 |
| Rich cultural institutions (museum, theatre, cinema …) | 3.6 | 3.791 | 3.791 | 3.691 |
| Public safety | 4.263 | 4.5811 | 4.256 | 4.337 |
| Political stability, stable government | 4.326 | 4.3815 | 4.318 | 4.337 |
| Economic stability | 4.326 | 4.381 | 4.318 | 4.337 |
| Social equality among population | 4.372 | 4.070 | 4.349 | 4.294 |
| Friendly, hospitable population | 4.302 | 4.209 | 4.341 | 4.29 |
| Not feeling discriminated | 4.427 | 4.209 | 4.295 | 4.344 |
| English commonly spoken | 4.206 | 4.163 | 4.023 | 4.152 |
| No need to learn a new language | 3.646 | 3.535 | 3.204 | 3.514 |
| Having high social status | 3.842 | 3.659 | 3.651 | 3.754 |
| Attractive taxation system | 3.687 | 3.651 | 3.386 | 3.607 |
| Quality and access to medical services (hospitals, family doctor) | 4.474 | 4.302 | 4.3784 | 4.411 |
| Social security and benefits (such as unemployment benefits, pensions) | 4.117 | 3.884 | 4.182 | 4.077 |
Note: The numbers in superscript show ranking of the five most important factors within a certain destination region.
| USA | Anglo-Saxon | European countries | Total | |
|---|---|---|---|---|
| Living near a large Indian community | 3.655 | 3.3816 | 3.0706 | 3.4545 |
| Easily bringing in my family now or later | 3.8143 | 3.5714 | 3.3024 | 3.6424 |
| I can easily return to later in my career | 4.212 | 3.9001 | 3.9772 | 4.0862 |
| Clear application procedure for residence and work permit | 4.241 | 3.8752 | 4.1141 | 4.131 |
| Accessibility of your spouse to the labour market | 3.5866 | 3.3855 | 3.1865 | 3.4486 |
| Being able to stay in a country longer than five years | 3.724 | 3.7753 | 3.4293 | 3.6653 |
| Possibility of permanent settlement | 3.2438 | 3.0738 | 2.8378 | 3.1128 |
| Possibility of acquiring local citizenship | 3.3017 | 3.1007 | 3.0937 | 3.217 |
Note: The numbers given in superscript designate the ranking of the most important factors within a certain destination region.
| Independent variables | Probability for choosing the United States | Probability for choosing the other Anglo-Saxon countries | ||
|---|---|---|---|---|
| Coefficient | St. Error | Coefficient | St. Error | |
| (Reference: education-motivated move) Work-related move | 0.848 | 0.647 | 1.827** | 0.871 |
| Other reasons to move | −1.052 | 1.785 | −1.443 | 2.890 |
| Female | 0.979 | 1.245 | 1.259 | 1.461 |
| (Reference: from a Hindu community) From a non-Hindu community | −0.003 | 1.151 | 0.581 | 1.518 |
| (Reference: single as a reference) In a relationship/married | −1.817** | 0.861 | −0.233 | 1.143 |
| Has children | −0.618 | 0.662 | −1.021 | 1.172 |
| (Reference: research-oriented universities) Practical/applied universities | 2.999 | 2.506 | −0.687 | 3.027 |
| (Reference: studies engineering) studies natural sciences | −1.863** | 0.837 | 0.010 | 1.163 |
| (Reference: enrolled in Bachelors programme) Enrolled in Masters programme | 3.700* | 1.974 | 2.152 | 2.031 |
| Doing a Ph.D. or Post-Doc | 4.550** | 2.120 | 1.212 | 2.242 |
| (Reference: parents prefer stay) Parents encourage move | −1.769** | 0.840 | −0.770 | 1.258 |
| Parents neutral to move | −1.532 | 1.328 | −17.513*** | 3.957 |
| (Reference: below average household income) Above average household income | −0.060 | 1.053 | 0.127 | 1.498 |
| (Reference: from an urban area) From a semi-urban area | −0.541 | 0.625 | 0.770 | 1.351 |
| from a rural area | 1.212 | 1.133 | 2.074 | 1.939 |
| (Reference: respondent never lived outside India) Lived outside India in the past | 1.609 | 1.026 | −0.173 | 1.109 |
| Parents have lived abroad | −1.691 | 2.154 | 2.032 | 2.175 |
| Siblings have lived abroad | 2.596 | 1.894 | 1.614 | 2.202 |
| Friends lived abroad | −1.239* | 0.694 | −0.603 | 0.921 |
| colleagues lived abroad | −1.074 | 0.896 | −1.089 | 1.243 |
| Importance of quality and content of work | 0.665 | 0.688 | −0.252 | 0.586 |
| Importance of good quality of education institutions | −0.108 | 0.810 | −1.906* | 1.128 |
| Importance of family-friendly environment | 0.752 | 0.488 | 0.801 | 0.530 |
| Importance of public safety | −0.765 | 0.561 | 0.822 | 0.895 |
| Importance of not having to learn a new language | 0.314 | 0.313 | 0.115 | 0.323 |
| Importance of being close to an Indian community | 0.679** | 0.320 | 0.226 | 0.482 |
| Importance of application procedures | 0.714* | 0.399 | −0.109 | 0.560 |
| Log likelihood | −65.407 | −65.407 | ||
| Pseudo R2 | 0.4214 | 0.4214 | ||
| Number of observations | 112 | 112 | ||
Notes: Comparison outcome is the probability to migrate to continental European countries. Significance levels *p < .1, **p < .05, ***p < .01; robust standard errors in the second column.
Disclosure statement
No potential conflict of interest was reported by the authors.
Metka Hercog is a scientific collaborator at Basel University and post-doctoral researcher on the project ‘The mobility of the highly skilled towards Switzerland’. Before joining Basel University in 2014, she had worked as a principal investigator on a research project examining skilled Indian migration and development at the Swiss Federal Institute of Technology (EPFL). She also worked at the University of Lausanne as the country coordinator for Switzerland on the project investigating mobility of Ph.D. doctorates in social science and in humanities. As a programme officer at the International Labour Organization (ILO), Dr Hercog worked on developing a standard methodology for assessing outcomes for migrant workers within bilateral migrant schemes. At present, her research centres on highly skilled mobilities, politics of mobility and migrants’ civic engagement. Dr Hercog received her M.Sc. degree in International Development Studies from Utrecht University (NL) and her Ph.D. degree in Public Policy from Maastricht University (NL).
Dr Mindel van de Laar is Ph.D. director of the dual career Ph.D. programme in Governance and Policy Analysis (GPAC2) of the Maastricht Graduate School of Governance, Maastricht University/UNU-MERIT. She supervises master and Ph.D. fellows with topics in the field of decision-making and higher education and capacity building. She offers Ph.D. education in research methods. She is chair of the MPP education committee, and institutional representative in the Association for Public Policy and Management (APPAM). As researcher, she worked specifically on the topic Foreign Direct Investment to developing countries, with focus on questionnaire development and analysis and macro-economic analyses. Currently she is working on research on capacity building in higher education, and innovative educational methods. Mindel holds a Ph.D. in economics and an M.Sc. in International Economic Studies from Maastricht University. Before her employment at MGSOG, she was employed by the Boston Consulting Group, she was global senior analyst for the Corporate Finance and Strategy Department, focusing on strategy issues related to mergers, acquisitions and alliances, using various large global M&A and alliances databases. She also worked on several international projects in transition countries in the field of social protection, both as consultant and researcher.
Notes
1. Those respondents who did not specify a preferred destination country or chose a country, which does not fit in the three geographical categories, were omitted from the analysis.
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