Who Comes Back and When? Return Migration Decisions of Academic Scientists Patrick Gaulé

Who comes back and when?

Return migration decisions of academic scientists

Patrick Gaule∗

September 29, 2013

Abstract

The net welfare benefit of ’brain drain’ of skilled workers depends on their propen-

sity to return to their home countries. Yet, relatively little is known empirically about

the return migration decisions of skilled workers. Here, I study a sample of 1,460 for-

eign faculty in research-intensive U.S. universities, using publicly available academic

records to reconstruct career histories and create a longitudinal panel. 7% of foreign

faculty in my sample returned to their home country during a period of 10 years on

average. Return occurs early in the career and is responsive to changes in income per

capita in the source country. I also investigate the effect of ability on the decision to

return with the evidence leaning towards positive, rather than negative, selection into

return migration.

Keywords: High-skilled Migration, Brain Drain, Scientists, Universities

JEL Classification: F22, I23, O15, O33, J61

∗Assistant professor, CERGE-EI, a joint workplace of Charles University and the Economics Institute ofthe Academy of Sciences of the Czech Republic, Prague, Czech Republic; patrickgaule@gmail.com. I thankRuchir Agarwal, Pierre Azoulay, Christian Catalini, Iain Cockburn, Tom Cunningham, Bill Kerr, JoshLerner, Fiona Murray, Mario Piacentini, Paula Stephan and Scott Stern for insightful comments, discussionsand advice. All errors are mine. I acknowledge financial support from the National Bureau of EconomicResearch Innovation Policy and the Economy Fellowship.

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1 Introduction

Scientists and engineers are increasingly mobile and often cross national borders. Nearly

half of the highly-cited physicists no longer work in the country in which they were born

(Hunter et al. 2009). More than a third of PhD holders in the U.S. Science and Engineering

workforce were born outside the U.S. (NSF 2007) and close to 60% of of engineering PhD

degrees recipients from U.S. universities hold temporary visas. Moreover, the foreign-born

make disproportionate contributions to U.S. science and innovation (Levin & Stephan 1999,

Hunt 2011, Gaule & Piacentini 2013). However, migration need not be permanent. For

instance, evidence from social security data compiled by Finn (2010) shows that 40% of

foreign PhD graduates from U.S. universities leave the country within five years.

Return migration decisions of scientists and engineers can be understood as the result of

utility maximization over the life cycle whereby migrants accumulate financial and especially

human capital in the destination before returning to their home country.1 Accordingly,

migrants are balancing the professional advantages of working in the destination country

versus psychic benefits from living in their home country. Psychic benefits include proximity

to friends and relatives, access to non-tradable goods and preferences for living in a society

that has certain cultural values. These psychic benefits can be large: Gibson & McKenzie

(2010) find that return migrants to New Zealand, Papua New Guinea and Tonga forfeit 40%

of their income flow by returning to their home country.

While the economics of return migration decisions are conceptually straightforward, we

know little about how the tradeoffs faced by skilled migrants play out empirically. How

many skilled migrants actually decide to return? Do migrants return early in the life cycle

as predicted by theory? To which extent do migrants respond to changing conditions in the

home country?

This paper seeks to answer these questions by studying individual return migration de-

cisions empirically. By focusing on academic scientists, I am able to use publicly available

academic records to reconstruct career histories. I rely on the availability of fine-grained

biographical data collected biennially by the American Chemical Society to guide students

in their choice of graduate schools. My hand-collected data includes 1,640 individuals and

1The alternative explanation for return migration is that migrant have incorrect expectations about theirprospects at destination. For a model of each explanation, see Borjas & Bratsberg (1996).

2

covers virtually every foreign faculty born after 1944 who has been affiliated with a U.S. PhD-

granting chemistry, chemical engineering or biochemistry department any time between 1993

and 2007.

Chemistry, biochemistry and chemical engineering account for 23% of U.S. Ph.D. degrees

in science and engineering granted by U.S. universities in 2006 (NSF 2009). Moreover, these

fields are rife with potential industrial applications, from drug discovery, development and

delivery to nanotechnology and clean technologies. Thus, while my data does not include all

U.S. Science and Engineering faculty, it covers an important and interesting subset of it.

During my period of observations which is 10 years on average, only 7% of individuals

in my sample return to their home country. Modeling the return migration decision as a

risk in a discrete hazard model, I find that (1) women are less likely to return, (2) strong

life cycle effects with the likelihood of return sharply decreasing after 50 and (3) immigrants

respond to changing conditions in the home country. The results on the effect of ability on

the decision to return are more mixed but the evidence leans towards positive, rather than

negative, self-selection into return migration.

While these results are descriptive, we see them as a useful step in the establishment of

a set of stylized facts that can be used to complement and inform the brain drain literature

that has developed almost exclusively on the theory side (see e.g. Bhagwati & Hamada 1974

and Mountford 1997 for influential contributions and Commander, Kangasniemi & Winters

(2004) for a review).

2 Measuring return migration

Most existing studies of return migration rely on data collected by statistical offices. However,

statistical offices are typically not good at following workers as they move across countries.

There is one notable exception in the form of the German Socio-Economic panel which has

been widely used (see e.g. Dustmann 1996, Dustmann & Kirchkamp 2002, Gundel & Heiko

2008). However, the evidence derived from the German Socio-Economic panel is not very

informative with respect with mobility of the high-skilled because migrants into Germany

3

are mostly unskilled.2

Thus, studying return migration empirically entails major measurement difficulties. The

approach taken here is to focus on a particular group of migrants, academic scientists, and

take advantage of the public availability of academic records. A variety of sources available

on the internet make it relatively easy to reconstruct career histories and track faculty who

go back home. The more difficult part of the data collection strategy is to find a good list

of academic scientists who have been at the destination in the past. This list cannot be too

recent otherwise very few returns will be observed. This list needs to have information about

the origins of scientists so that migrants can be identified by country of origin. Moreover, it

is highly desirable for this list to be available for successive years, otherwise migration spells

of relatively short duration cannot be observed.3

The main source of data used in this paper is the Directory of Graduate Research of the

American Chemical Society. The American Chemical Society surveys U.S. PhD-granting

chemistry departments every two years with the aim of providing information to prospective

graduate students. Because the size of the department is an important factor in the choice

of a graduate school, departments have an incentive to list faculty extensively. Comparisons

with lists of faculty on departmental websites suggest that the coverage of the directory is

excellent. The resulting directories are published in print and, from 1999 onwards, on the

web.4 The individual faculty listings from the directory have information on names, year of

birth, gender, education histories, postdoctoral training and university affiliation.

From the individuals who appear in the directory at least once between 1993 and 2007,

I select the sub-sample of faculty who had received an undergraduate degree from a foreign

2The mean number of years of schooling for immigrants in the panel is 9 with a standard deviation of 2(Dustmann 1996).

3To illustrate these points, consider two alternatives which would be inadequate for my purposes. A listof faculty employed by U.S. PhD-granting departments as of 1993 is available from the National ResearchCouncil Assessment of doctoral programs (NRC 1995). One of several problems with using this data isthat neither migrants who were hired after 1993, nor those who were hired before 1993 and returned before1993 appear in the list. Another approach would be to construct lists of foreign students graduating fromU.S. universities with PhD degrees in Science and Engineering by using biographic information containedin doctoral theses (Mac Garvie 2007, Kahn & Mac Garvie 2010) or by inferring origins from names (Gaule& Piacentini 2013). However, following students over time and distinguishing those who return home fromthose who take industry jobs in the U.S. is difficult.

4A CD containing the 1991 and 1993 editions was also produced. Electronic version were used whenavailable. The 1995 and 1997 print versions of the directory were inspected and coded manually since noelectronic version was available

4

country and were born after 1944. From that sample, I exclude individuals that appear in

the 1991 edition of the directory to make sure that I am following individuals since their first

faculty appointment in a U.S. PhD- granting department. In the absence of information on

the country of birth, the home country is assumed to be the country where the undergraduate

degree was obtained. This is not a major limitation because the country of undergraduate

degree is a good proxy for the country of origin.5

I reconstruct career histories by combining information from successive editions of the

directory. For individuals who cease to be listed in the directory, I conduct extensive manual

search to distinguish between returns to the home country, moves to a third country, moves to

industry, moves to U.S. academic jobs not listed in the directory, and deaths and retirement.

A combination of Google and LinkedIn searches are used. In some cases, publication data

serve as as an ancillary source. The results of this data collection effort are very encouraging

as I am able to reconstruct histories for all but 31 individuals (2.1%) of the sample. These

individuals are excluded from the subsequent analysis.6

The end result is a panel of 1,460 individuals with fine grained biographical information.

One limitation is that only faculties from chemistry, chemical engineering and biochemistry

departments, and not all fields of Science and Engineering are included in the data set.

However, within the included fields, the coverage is excellent and not biased towards faculties

who have been particularly successful or have had longer migration spells.

3 Data

I start observing individuals when they are first listed ACS Directory of Graduate Research

(1993 to 2007 editions) until 2013. On average, an individual is observed for 11.5 years.

The data includes 1,460 faculty. Of these, 912 (61.2%) do not go through any professional

5Evidence compiled by Kahn and McGarvie (2010) using the Survey of Earned Doctorates shows thatamong the the non-US citizen graduating from U.S. universities in 2003 and 2004, 85% did their undergrad-uate studies in their country of citizenship I expect this fraction to be higher for my sample because theaverage faculty in my dataset is older and international mobility for undergraduate studies has increasedover time.

6If these individuals have in fact returned to home country, excluding them from the sample wouldintroduce measurement error. However, most of those have no publishing activity which suggests eithermoves to industry or death.

5

transition during the period of observations. 256 (17.2%) move to different U.S. academic

positions and only 104 (7.0%) return to the home country. Moves to U.S. jobs outside

academia and moves to third countries are infrequent (4.6% and 2.6% respectively). The

overwhelming majority of return migrants take an academic position in their home country.

Inevitably, given the way the data was constructed, there is a group of individuals with

missing career histories. This is problematic if the reason I am unable to reconstruct their

career histories is that they have returned to their home country. However, there only 29

individuals with missing career histories (I exclude those from the sample). Moreover, in

most of those cases, I strongly suspect either a death or a move to industry, because there

are no subsequent publications.

(Insert table 1 about here)

The ACS directory include information on gender, year of birth and the school where the

individual completed his/her undergraduate and graduate studies. Data on home country

GDP per capita in constant terms and adjusted for PPPs is obtained from the World Bank

World Development Indicators. As a proxy for school ranking, I use the scores of the 1995

NRC evaluation of chemistry graduate programs (NRC 1995). I define top schools as those

having a score of 4 or higher in that evaluation, i.e, Berkeley, Harvard, MIT, Stanford, Cal-

tech, Cornell, Columbia, University of Chicago, University of Wisconsin, UCLA, University

of Illinois, Yale, University of Texas, Northwestern, Texas A& M university, UC San Diego,

Penn State and the University of North Carolina.

Finally, I also match scientists to their publication records in Scopus, a bibliographic

database with extensive coverage of science and engineering.7 This gives me yearly publica-

tion output which I weight by journal impact factor.8 For every individual-year, I compute

the average productivity from the first faculty appointment the current year. Outstanding

productivity is defined as having an average productivity that exceeds the sample mean by

more than 2 standard deviations.

The average scientist in my dataset 2 is male, was born in 1963 and was first listed in the

ACS directory (i.e. started his career as faculty) in 2001. The majority (52%) of faculty in

7The following criteria are used to match individuals in my sample to their publication records: lastname, first initial, nonmissing middle initial, university and ‘chem’ string in the author’s address

8Journal impact factors are separately obtained from ISI Web of Knowledge.

6

the data have a U.S. PhD degree, with 20% graduating from a a top U.S. school as defined

earlier.

(insert table 2 about here)

Compared to the full risk set, the group of returnees has a smaller fraction of women, a

lower fraction of migrants with U.S. PhD degree. In terms of origins, the sources countries

with the largest groups of migrants are China, India, the United Kingdom, Germany, Canada

and Russia (figure 1). In terms of the incidence of return, the rate is relatively high for

Western European countries, Canada and Australia and very low for China and India (figure

3). For more details on variation across source countries see table 4.

(insert table 4 about here)

(insert figure 1, 2 and 3 about here)

The case of China is of special interest, both due to the size of the country, the expansion

in the higher education sector that occurred in the last two decades, and returns programs

launched to repatriate Chinese expatriated talent.9 In my sample, return to China is rare-

although increasing. I have 10 returns to China out of risk set of 269, and 8 of those have

occurred since 2007. By comparison, return to Taiwan or Korea is five times more likely

than return to China (see table 4).

4 Methodology and results

I model the decision to return using a discrete-time hazard model for the decision to return.

An individual in my sample is assumed to be ’at risk’ of returning to the home country

every year up until the end of the observation period (i.e. in 2013) or up until the individual

returns to the home country, dies, or retires (if earlier than 2013).

9The higher education system in China dramatically expanded in the last decade with the number ofundergraduate and graduate students in China growing at approximately 30% per year since 1999 (Li et al.2008). Particular efforts have been deployed to bring a dozen elite Chinese institutions to world class statusunder project 985 (ibid.). A series of programs have been launched to attract migrants back home - “HundredPeople”, “300 Talents”, “Changjiang Scholars”, “Outstanding Overseas Talent”, “Thousand-Person Plan”(Normile 2000, Xin 2009). The salaries promised to returnees under these programs are large by Chinesestandards. For instance, under the Changjiang Scholars launched in 1999, a recipient receives an annualsalary four times as large as that of a typical professor (Normile 2000).

7

Let πit = Pr[returnit|returnit′,t′<t = 0, Xit]. My specifications are logistic functions of

the form:

Log[πit

(1− πit)] = αt + β′Xit + γh(t) (1)

where αt is a set of indicator variables for time (five-year bin) and Xit is a vector of

independent variables, including indicator variables for age, gender, having a PhD degree

from the US, past productivity, calendar year fixed and country fixed effects. The hazard

function h(t) is taken to be a third-order polynomial in the number of years since the indi-

vidual is first listed in the directory. When a return has occurred, the subsequent years of

observations are dropped from the sample.

(insert table 3 about here)

The odds ratio estimated through the logistic regression on the propensity to return are

reported in table 3. Column 1 consider basic demographic characteristics. Women are less

likely to return. The effect is large (-48%) although only marginally significant given the

small number of women in the data. Women working in academia are often thought to have

more constraints on mobility than men (Stephan 2012) which is consistent with my result.

The odds or return are also significantly lower for individuals with a U.S. degree, which

is hardly surprising since those individuals have had longer migration spells, and thus less

attachment to the home country.10 The coefficients on age bins reveal interesting life cycle

effects: the incidence of return migration decreases very substantially around the age of 50.11

These effects are consistent with return migrants first accumulating human (or other) capital

and then go back home where they enjoy a higher utility of consumption.

In column 2-4 of table 3, I investigate the correlation between various measures of ability

and the decision to return. Individuals who have been particularly productive (i.e. whose

average yearly productivity is more than twice above the sample mean) during their U.S.

appointments are significantly more likely to return. The effect of having graduated from

an elite PhD program is positive but not significant (column 3) while the odds of return are

lower for those who have an appointment at an elite PhD program (column 4) although the

10In addition, or alternatively, the decision to study in the U.S. in the first place could be driven a lowerpreference for living in the home country

11The life cycle patterns are also apparent in the raw data (see figure 4).

8

effect is not significant either.

The results on the effect of ability on the decision to return should be treated with

caution. The three measures considered do not paint a completely coherent picture (with

two measures suggesting a positive correlation and one a negative correlation). Moreover,

the results are necessarily somewhat sensitive to alternative ways of measuring and defining

ability. Overall though, the balance of evidence is more consistent with positive, rather

negative, selection into return migration.

Finally, in column 5, I analyze the relationship between the difference in GDP per capita

between the home country and the United States (in 1000’s constant PPP-adjusted 2005

dollars) and the propensity to return. GPD per capita is probably not the most relevant

home characteristic and investigating the role of different home country characteristics is

beyond the scope of this paper given sample size and data limitation. Instead, I seek simply

to assess whether migrants respond to changing conditions in the home country when making

return migration decisions. GDP per capita can be thought as a very rough proxy for salaries

and will also be correlated with the quality of the health system, infrastructure and education

in the home country. The coefficient on the GDP per capita difference in table 5 is positive

and significant. Since source country fixed effects are included in the specifications, this

can be interpreted as evidence that when economic conditions in the home country improve

relatively to those in the United Source, immigrants are more likely to return.

5 Conclusion

The location decisions of the scientists implicitly reveal their preferences. In my sample, the

incidence of return migration is small and thus for the vast majority of scientists who stay

in the U.S. either (a) the disutility of living in the U.S. relative to the home country is lower

(in absolute value) than the professional advantages, pecuniary, reputational or otherwise,

of working in the U.S. or (b) there is no disutility of living in the U.S. relative to the home

country.

This fact cast doubts on the suggestion from recent theoretical or qualitative papers

that benefits from return migration may be large enough to outweigh the costs of the brain

drain for source countries (Mayr & Peri 2008, Dustmann, Fadlon & Weiss 2010, Santos &

9

Postel-Vinay 2003, Saxenian & Hsu 2001, Saxenian 2005). On the other hand, the fact that

individuals with outstanding productivity are more likely to return is better news for the

home country.

I conclude by suggesting two areas for future research which are closely related to this

study.

The first relates to the location decisions of migrant scientists and engineers at the end

of their graduate and/or postgraduate training. Finn (2010) uses Social Security Data to

measure the percentage of foreign graduate students who are no longer in the U.S. five

years after graduation. The resulting number, around 40%, is much higher than the rate of

return in my sample. This must reflect in large part a life-cycle effect: international mobility

decisions are mostly determined early in the career. However, how does ability influence the

decision to return at the end of graduate and/or postgraduate training? Do the brightest

and most promising foreign young scientists choose to stay in the U.S. or do their return to

their home country?

Second, which effects does return migration have on the return destination in terms of

the quality of research, the training of students, and ultimately local entrepreneurship and

innovation? The answers to such questions have important implications for our understand-

ing of knowledge diffusion across countries but also, from a policy perspective, for how much

sources countries should invest in programs to attract more returnees.

10

References

Agrawal A, Kapur D, McHale J & Oettl A (2011) “Brain Drain or Brain Bank? The Impact

of Skilled Emigration on Poor-Country Innovation” Journal of Urban Economics 69(1):43-55

Bhagwati J, & Hamada K (1974) “The brain drain, international integration of mar-

kets for professionals and unemployment: a theoretical analysis” Journal of Development

Economics, 1(1), 19-42.

Borjas G & Bratsberg B (1996) “Who Leaves? The Outmigration of the Foreign-Born”

Review of Economics and Statistics 78(1):165-176

Commander S, Kangasniemi M & Winters L (2004) ”The brain drain: curse or boon?

A survey of the literature.” In Challenges to Globalization: Analyzing the Economics (pp.

235-278). University of Chicago Press.

Commander S, Chanda R, Kangasniemi M, Winters A (2008) “Must Skilled Migration

Be Brain Drain?” The World Economy 31(2):187-211

Cox D (1972) “Regression models and life-tables” Journal of the Royal Statistical Society,

Series B 34:187-220

Dustmann C & Kirchkamp O (2002) “The Optimal Migration Duration and Activity

Choice after Re-migration” Journal of Development Economics 67(2):351-372

Dustmann C (1996) “Return migration: the European experience” Economic Policy

11(22):213-250

Dustmann C & Weiss Y (2007) “Return Migration: Theory and Empirical Evidence from

the UK” British Journal of Industrial Relations 45(2):236-256

Dustmann C, Fadlon I, Weiss Y (2011) “Return migration, human capital accumulation

and the brain drain” Journal of Development Economics 95(1):58-67

Finn M (2010) “Stay Rates of Foreign Doctorate Recipients from the U.S. Universities

2010” Oak Ridge, TN: Oak Ridge Institute for Science and Education, (and other years)

Gaule P & Piacentini M (2013) “Chinese graduate students and US scientific productiv-

11

ity” Review of Economics and Statistics 95(2): 698701

Gibson J & McKenzie D (2010) “The Microeconomic Determinants of Emigration and

Return Migration of the Best and Brightest: Evidence from the Pacific” Journal of Devel-

opment Economics. Forthcoming.

Gundel S & Heiko P (2008) “What Determines the Duration of Immigrants in Germany?:

Evidence from a Longitudinal Duration Analysis” International Journal of Social Economics

35(11):769-782

Hunt J (2011) “Which Immigrants Are Most Innovative and Entrepreneurial? Distinc-

tions by Entry Visa” Journal of Labor Economics 29(3):417-457

Hunter R, Oswald A, Charlton B (2009)“The Elite Brain Drain” Economic Journal

119(6):F231-F251

Kahn S & MacGarvie M (2010a) “How Important Is U.S. Location for Research in Sci-

ence?” mimeo Boston University.

Kahn S & MacGarvie M (2010b) “The Effect of the Foreign Fullbright Program on

Knowledge Creation in Science and Engineering” Rate and Direction of Inventive Activity

50th Anniversary Proceedings. Forthcoming. Cambridge, MA: National Bureau of Economic

Research

Kerr W (2008) “Ethnic Scientific Communities and International Technology Diffusion”

Review of Economics and Statistics 90(3):518-537

Levin S & Stephan P (1999) “Are the Foreign Born a Source of Strength for U.S. Science?”

Science 285(5431):1213-1214

Li Y, Whalley J, Zhang S & Zhao X (2008) “The Higher Educational Transformation of

China and Its Global Implications” NBER Working Paper 13849. Cambridge, MA: National

Bureau of Economic Research

Mayr K & Peri G (2008) “Return Migration as a Channel of Brain Gain” NBER Working

Paper 14039. Cambridge, MA: National Bureau of Economic Research

MacGarvie M (2007) “Using Published Dissertations to Identify Graduates’ Countries

of Origin.” Unpublished manuscript prepared for presentation at the NBER Conference on

12

Career Patterns of Foreign-born Scientists and Engineers, November 7, 2007.

Mountford A (1997) “Can a brain drain be good for growth in the source economy?”

Journal of Development Economics 53:287-303

NRC (1995) “Research Doctorate Programs in the United States: Continuity and Change”

Washington, DC: National Academies Press.

NSF (2007) “Asia’s Rising Science and Technology Strength: Comparative Indicators for

Asia, the European Union and the United States” National Science Foundation, Division of

Science Resources Statistics. NSF-07-319. Arlington, VA.

NSF (2009) “Science and Engineering Doctorate Awards: 2006” National Science Foun-

dation, Division of Science Resources Statistics. NSF-09-311. Arlington, VA.

Normile D (2000) “Human resources: New Incentives Lure Chinese Talent Back Home”

Science 287(5452):417-418

Rumbley L, Pacheco, I & Altbach, P (2008) “International Comparison of Academic

Salaries: An Exploratory Study” Boston: Boston College Center for International Higher

Education

Santos M, Postel-Vinay F (2003) “Migration as a source of growth: the perspective of a

developing country” Journal of Population Economics 16:161-175

Saint-Paul G (2004) “The Brain Drain: Some Evidence from European Expatriates in

the United States” IZA Discussion Paper #1310.

Saxenian A (2005) “From Brain Drain to Brain Circulation: Transnational Communities

and Regional Upgrading in India and China” Studies in Comparative International Devel-

opment 40(2):35-61

Saxenian A & Hsu J (2001) “The Silicon Valley Hsinchu Connection: Technical Commu-

nities and Industrial Upgrading” Industrial and Corporate Change 10(4):893-920

Stephan, P (2012). How economics shapes science. Harvard University Press.

Xin H (2009) “Help Wanted: 2000 Leading Lights To Inject a Spirit of Innovation”

Science 325(5940):534-535

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Table 1: Professional transitions of foreign faculty

Still in the U.S. in 2013

No professional transition 912 61.2%Moved to a different U.S. academic position 256 17.2%Took a job in U.S. industry or government 68 4.6 %

No longer in the U.S. by 2013

Returned to the home country 104 7.0%Moved to third countries 39 2.6%

Censored observations

Died or retired 81 5.4%

Excluded

Individuals whose careers could not be reconstructed 31 2.1%Total 1,491 100%

Notes: The sample consists of faculty with a non-US undergraduate degree born after 1945that are listed in the American Chemical Society (ACS) Directory of Graduate Research (1993to 2007 editions). Career histories are reconstructed by consolidating the 1993 to 2007 editionsof the directory and through manual google searches. Individuals listed in the 1991 version ofthe directory are excluded since for those I would observe only those who stayed until 1993.Death and retirement are treated as censoring events (i.e. individuals are dropped from thesample after the event occurs). On average, an individual is observed for 11.5 years. Thereis a group 31 individuals (2.1%) for which career histories could not be reconstructed. Theseindividuals are excluded from the subsequent analysis.

Table 2: Demographic - sample means

Full risk set Returnees(n=1,460) (n=104)

Female 0.16 0.09Born 1963 1963First listed in ACS Directory of Graduate Research 2001 2000U.S. PhD degree 0.52 0.42Graduated from top U.S. schools 0.20 0.24

Notes: Top school are defined as those received a score of 4 or higher in the 1995 NRC eval-uation of chemistry graduate programs: Berkeley, Harvard, MIT, Stanford, Caltech, Cornell,Columbia, University of Chicago, University of Wisconsin, UCLA, University of Illinois, Yale,University of Texas, Northwestern, Texas A& M university, UC San Diego, Penn State andthe University of North Carolina.

14

Table 3: Selection into return migration

(1) (2) (3) (4) (5)

Female 0.495∗∗ 0.517∗ 0.503∗ 0.490∗∗ 0.399∗∗

(0.174) (0.183) (0.177) (0.173) (0.158)U.S. degree 0.499∗∗∗ 0.491∗∗∗ 0.392∗∗∗ 0.515∗∗∗ 0.511∗∗∗

(0.125) (0.124) (0.119) (0.130) (0.131)Oustanding productivity 2.195∗∗

(0.840)Graduated from top school 1.637

(0.511)Appointment at top school 0.721

(0.258)GDP per capita difference 1.041∗

(0.024)Age from 30 to 34 3.035 3.107 2.935 3.063 2.369

(2.155) (2.206) (2.083) (2.175) (1.681)Age from 35 to 39 3.500∗∗ 3.507∗∗ 3.398∗∗ 3.509∗∗ 2.749∗

(2.032) (2.036) (1.971) (2.036) (1.596)Age from 40 to 44 4.093∗∗ 4.106∗∗ 4.031∗∗ 4.085∗∗ 3.433∗∗

(2.277) (2.284) (2.241) (2.272) (1.908)Age from 45 to 50 3.172∗∗ 3.166∗∗ 3.170∗∗ 3.159∗∗ 2.850∗

(1.788) (1.784) (1.785) (1.780) (1.615)Age from 50 to 55 1.189 1.186 1.191 1.186 0.977

(0.807) (0.805) (0.808) (0.805) (0.700)

Polynomial hazard function Yes Yes Yes Yes YesHome country FE Yes Yes Yes Yes YesCalendar year FE (5-year bin) Yes Yes Yes Yes Yes# of individuals 1,221 1,221 1,221 1,221 1,176# of observations 13,785 13,785 13,785 13,785 13,132

Notes: *p < 0.1, ** p < 0.05, *** p < 0.01. The omitted categories are: male, more than 60years old; foreign degree. Outstanding productivity is defined as having an average productivityup until the current year exceeding the sample mean by two standard deviations or more. Topprograms are defined as those received a score of 4 or higher in the 1995 NRC evaluation ofchemistry graduate programs: Berkeley, Harvard, MIT, Stanford, Caltech, Cornell, Columbia,University of Chicago, University of Wisconsin, UCLA, University of Illinois, Yale, Universityof Texas, Northwestern, Texas A& M university, UC San Diego, Penn State and the Universityof North Carolina. The GDP per capita difference is the difference in GDP per capita betweenhome country and the U.S measured in thousands of 2005 PPP dollars. The estimation methodis a logistic regression, reporting odds ratio. Thus, a point estimate of less than 1 indicates anegative effect. For instance, women are 47.2% less likely to return than men. The number ofobservations and individuals is lower than in the the descriptive become some country of originfixed effects (for instance India) perfectly predict failure to return and those observations arenot used in the estimation.

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Table 4: Incidence of return migration across source countries

(1) (2) (3) (4)Home country # individuals # returns incidence odds ratioChina 279 10 0.03 1.39India 143 0 0 0Germany 134 14 0.10 3.01United Kingdom 127 5 0.39 1Canada 118 13 0.11 4.17Russia 103 2 0.19 0.57Greece 63 7 0.11 4.02Korea 62 10 0.16 8.61Taiwan 37 5 0.13 7.49Australia 36 6 0.17 6.01Argentina 33 2 0.06 2.37Switzerland 29 4 0.14 3.63Japan 25 3 0.12 4.17France 23 3 0.13 4.22Poland 23 1 0.04 2.34Israel 23 3 0.13 4.54Netherlands 21 0 0 0Italy 20 0 0 0

Notes: Column 1 indicates the number of individuals from a particular source country observedin my sample who are at ’risk’ of returning. Countries with less than 20 individuals are notshown in this table. Column 2 is the number of actual returns observed. Column 3 is theincidence ratio (IR) which is equal to column 2 divided by column 1. Column 4 displaysthe fixed effects from the baseline logistic regression with demographic characteristics(table 3column 1). These are expressed in terms of odds ratio relative to the United Kingdom. Forinstance, return to Australia is six times more likely than return to the United Kingdom.

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Figure 1: Size of the diaspora by country

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1 276diasporas

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Figure 2: Number of returns by country

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1 14returns

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Figure 3: Incidence ratio of return migration by country

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0 50percentage of returns

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Figure 4: Age at return

05

1015

20Fr

eque

ncy

30 40 50 60 70Age at return

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