Brain drain and economic growth: evidence of productivity growth from brain circulation

Abstract

This paper analyzes the effect of the so-called ‘brain drain’ on economic growth through the channel of growth in total factor productivity. We analyze panel data that measure the severity of brain drain, which are from IMD and the U.S. National Science Foundation. Our analysis shows that middle-income countries have more brain drain compared to the group of high-income countries. Also, emerging economies that grow fast tend to experience more brain drain. Our results from fixed effects regression models show that that brain drain has a significant and positive impact on economic growth, and the main channel is productivity growth. This can be considered as evidence of the positive effects of ‘brain circulation’, which is one of the brain drain phenomena that settlement of the talents in advanced countries can eventually help improve the productivity of home country by the sharing of advanced technologies and skills around them with colleagues in motherland. Therefore, a strategy of utilizing overseas resident talents should also be considered, alongside the brain-attraction policy.

Full text

European Journal of Government and Economics 10(2), December 2021, 128-145
European Journal of Government and Economics
ISSN: 2254-7088
Brain drain and economic growth: evidence of productivity growth
from brain circulation
Woosik Yua*
a Graduate School of International Studies, Seoul National University, Seoul, Republic of Korea
* Corresponding author at: woosik@snu.ac.kr
Abstract. This paper analyzes the effect of the so-called ‘brain drain’ on economic growth through the channel of growth
in total factor productivity. We analyze panel data that measure the severity of brain drain, which are from IMD and the
U.S. National Science Foundation. Our analysis shows that middle-income countries have more brain drain compared to
the group of high-income countries. Also, emerging economies that grow fast tend to experience more brain drain. Our
results from fixed effects regression models show that that brain drain has a significant and positive impact on economic
growth, and the main channel is productivity growth. This can be considered as evidence of the positive effects of ‘brain
circulation’, which is one of the brain drain phenomena that settlement of the talents in advanced countries can eventually
help improve the productivity of home country by the sharing of advanced technologies and skills around them with
colleagues in motherland. Therefore, a strategy of utilizing overseas resident talents should also be considered, alongside
the brain-attraction policy.
Keywords. brain drain, brain circulation, economic growth, total factor productivity
JEL Codes. O11 ; O15; O47; F43
DOI. https://doi.org/10.17979/ejge.2021.10.1.7831
1. Introduction
As globalization deepens, issues with brain drain are often discussed worldwide. Brain drain
refers to a state or a phenomenon in which a country cannot utilize its effective labor of human
resources or human capital. This phenomenon is largely divided into internal brain drain and
external brain drain. Internal brain drain refers to the phenomenon in which domestic personnel
with specialized skills are engaged in jobs unrelated to their majors, and external brain drain refers
to the state in which competent talents of a country reside in other foreign countries, and their
productivity contributes to the production of other countries. The brain drain to be addressed in
this study is the second concept, the external brain drain.
Whenever the ‘Brain Drain Index’ is announced at the International Institute for Management
Development (IMD) in Switzerland every year, developing countries have continuously raised
serious questions about the negative impact of brain drain on economic growth as well as national
productivity and competitiveness. The argument is that the more serious the outflow of competent
human resources to foreign countries, the less experts and research personnel in the home
country, and this results in the weakening of the country’s research and technology
competitiveness and reduction of innovation, which in turn adversely affects economic growth.

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On the contrary, some argue that brain drain may have a positive effect on the national
economy and competitiveness. In South Korea during the 1970s, the ‘brain drain’ had even been
regarded as an act of ‘traitoring’ and the students residing abroad after the completion of their
studies were harshly blamed. However, in recent days, as described in Saxenian (2005) and
Teney (2021), the practical concept of ‘brain circulation’ came up in which the students or workers
who studied and were employed abroad do not return to their home country right away, but return
after working abroad and acquiring advanced learning and skills, or contributing to the motherland
through various human networks built up while living abroad. In this regard, brain drain is
considered a positive phenomenon as the first step for the brain circulation.
As such, there are not many studies that quantitatively analyze the direct relationship between
brain drain and economic growth using growth regression models, although brain drain can be a
major factor in economic growth, either through the leakage of human capital or through the
technological progress from brain circulation. Therefore, in this study, we intend to supplement
the prior study by conducting an empirical analysis of the effects of external brain drain on
economic growth using growth regression models, while looking at the time series trend for brain
drain situations of several selected countries.
2. Literature review
Studies on the negative effects of the flight of skilled workers on the national economy and
competitiveness are conducted by Grubel and Scott (1966), Bhagwati and Hamada (1974), and
McCulloch and Yellen (1977). In addition, Docquier and Rapoport (2008) presented empirical
analysis that since the 1990s, the migration of skilled workers (brain drain) has increased by a
greater margin than that of ordinary workers, and explained the factors behind the brain drain.
However, studies by Mountford (1997), Stark, Helmenstein, and Prskawetz (1997), Beine,
Docquier and Rapoport (2001), and Cinar and Docquier (2004) suggest that the migration of the
talents to advanced technology-holding countries could have a positive impact on economic
growth and competitiveness in the home country. According to Beine et al. (2001), immigration of
talents can have both a ‘brain effect’ when returning home after acquiring advanced technology
and a ‘drain effect’ that occurs when not returning home, and when brain effect is large enough
under the open economy model, brain drain can have a positive effect on economic growth. Stark
et al. (1997) also presented a research that said the dissemination of skills and knowledge gained
in advanced technology-holding countries could help improve the home country’s technological
prowess when migrant talent returns. Cinar et al. (2004) also argued that the home remittance of
competent people who moved to developed countries had a positive effect on economic growth
of the home country, as is often seen in the cases of immigration of workers from developing
countries. Moreover, Vidal (1998) explored brain drain’s positive effect on human capital formation,
and Saxenian (2005) described the process of brain circulation from brain drain with lowered
transaction costs associated with digitization, and how it ultimately helps the development of

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technology in home country, using the data of Chinese- and Indian-born engineers in the United
States (U.S.).
More recently, Kang, Lim, and Hwang (2013) provided empirical evidence of brain drain’s
indirect but positive influence on national competitiveness through R&D investment and
accumulated human capital. Also, Lodigiani, Marchiori, and Shen (2015) showed that brain drain
could have both positive and negative effects on GDP per capita through the technology diffusion
at origin from the high-skilled diaspora, depending on the region’s relative distance to the
technological frontier.
Amid this widespread agreement and objection to the severity of brain drain, this study seeks
to infer the effects of brain drain on economic growth and technological growth (a possible channel
to economic growth) of home country, using the data that proxies the degree of brain drain.
3. Motivation
We were intrigued by the concept of ‘brain circulation’, one of the positive results from brain drain.
As described in the above sections, talents who studied and were employed abroad can benefit
their motherland through their acquisition of frontier technology and skills and sharing of their
knowledge to their colleagues living in motherland, or co-workings with colleagues in home
country.
With an awareness that brain drain can adversely affect society in the sense of human capital
flight, we hypothesized that brain drain could also have a positive effect on economic growth, as
many other researchers claim. However, our main argument is more comprehensive. We
hypothesize that brain drain helps achieve economic growth through productivity growth. Brain
drain can induce brain circulation, and brain circulation helps home country’s technological
progress. Technological progress ultimately improves productivity of production, and results in
growth of income.
Particularl y, the total factor productivity (TFP) is predicted to be mostly affected by the brain
drain via brain circulation and technology diffusion. TFP refers to the productivity of an economy’s
composite inputs that are used to produce national output, and in neoclassical growth theory,
economic growth rate converges to the growth rate of TFP in the long run (steady state). Thus,
TFP can be a proxy for a country’s productive efficiency and technological level.
TFP growth is one of the key components and channels for the economic growth in a simple
growth model of = where  is national income,  is total factor productivity,  is
composite input, and  is population. From the model, the income growth can be derived:

= ∙ 
 [1]
Then, 
= is national income per capita and 
= is composite input per capita. When
converting to growth terms, national income per capita growth () can be expressed as the sum

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of growth of total factor productivity (  ) and growth of per-capita composite input (  ) as
described in equation [2]:
=+ [2]
This implies that there are two main channels to which economic growth can be achieved:
increasing growth rate of TFP or growth of composite input per capita. As the key hypothesis of
this paper is that brain drain may enhance technology and productivity of home country through
technological diffusion, testing the effects of brain drain on TFP growth will be the focus of our
empirical analysis, as a channel to achieve income growth.
4. Empirical landscape
Before analyzing the data, we will look at the main explanatory variables of the paper along with
the descriptive statistics (see Table 1). In this study, two indicators are used as key variables that
can proxy the degree of brain drain. The first data was established using the ‘Brain Drain Index’
survey data from the World Competitive Executive Opinion Survey of the International Institute
for Management Development (IMD) in Switzerland. The criteria number in IMD Competitiveness
Yearbook is ‘3.2.21’ and its title is ‘Brain drain’. The survey question is “Brain drain ([of ]well-
educated and skilled people) does not hinder competitiveness in your economy”1 with a scale
from zero to ten, and is asked to influential entrepreneurs worldwide. Recent years of data can
be downloaded online at website of IMD World Competitiveness Online 2, and the past data can
be found in IMD World Competitiveness Yearbook series. As mentioned above, the index values
of the original data are distributed from 0 to 10, and the more severe the brain drain is, the more
negative impact it has on economic and management activities, and the closer it becomes to zero.
In this study, the corresponding brain drain index values were subtracted from 10, so that the
higher the brain drain is, the higher the values are. This newly defined IMD brain drain index is
referred to as the ‘brain drain index’ in this paper. The indicator covers data from 61 countries and
provides a total of 22 years of time series values from 1995 to 2016. This data is important in the
sense that it can give information on how the entrepreneurs feel about brain drain of the country,
as the entrepreneurs are the front-line agents who put the up-to-date technologies into practical
uses, and they are eager to employ the talented the most.
1 The IMD World Talent Ranking Methodology (2019)
2 https://worldcompetitiveness.imd.org/CustomSearch

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Table 1. Descriptive statistics of brain drain index and share of doctorate recipients staying in the U.S.
Sample
Size Mean Standard
Deviation Minimum Maximum No. of
Countries Period
Brain drain index3 1,174 4.779 1.582 1.04 8.85 61 1995~2016
PhD ratios staying
in the U.S. 287 0.628 0.167 0.10 0.92 41 2010~2016
Brain drain index 210 5.379 1.322 2.75 8.85 30
(common
sample
counties)
2010~2016
(common
sample
period)
PhD ratios staying
in the U.S. 210 0.609 0.145 0.21 0.92
Note. Third and fourth rows are for common sample period (2010~2016) and sample country list (30 countries). Source:
IMD World Competitiveness Executive Opinion Survey, US National Science Foundation
Table 2. Rankings of IMD Brain Drain Index by Country in 20164.
Rank Country B.D. Index Rank Country B.D. Index Rank Country B.D. Index
1 Hungary 8.44 22 Lithuania 5.97 43 Canada 4.20
2 Venezuela 8.44 23 Argentina 5.90 44 Israel 4.17
3 Bulgaria 8.04 24 Mongol 5.83 45 Iceland 4.08
4 Ukraine 8.00 25 Jordan 5.78 46 Chile 3.94
5 Croatia 7.84 26 Brazil 5.73 47 Belgium 3.89
6 South Africa 7.23 27 Japan 5.58 48 Indonesia 3.76
7 Slovakia 7.13 28 Spain 5.49 49 Germany 3.64
8 Kazakhstan 7.05 29 Mexico 5.23 50 Britain 3.58
9 Slovenia 6.85 30 France 5.22 51 Singapore 3.51
10 Russia 6.75 31 Italy 5.14 52 Finland 3.46
11 Greece 6.68 32 India 5.09 53 UAE 3.38
12 Portugal 6.61 33 Ireland 4.93 54 Luxembourg 3.35
13 Latvia 6.58 34 Rumania 4.55 55 Netherlands 2.80
14 Poland 6.58 35 Turkey 4.49 56 Hong Kong 2.78
15 Estonia 6.51 36 Cech 4.46 57 Denmark 2.75
16 South Korea 6.40 37 Thailand 4.41 58 Sweden 2.70
17 Taiwan 6.31 38 Qatar 4.41 59 USA 2.67
18 Philippines 6.23 39 Austria 4.35 60 Swiss 2.03
19 Columbia 6.07 40 Australia 4.31 61 Norway 1.68
20 China 6.05 41 New Zealand 4.30
21 Peru 5.98 42 Malaysia 4.28
Note. Source: IMD World Competitiveness Executive Opinion Survey.
3 Values were subtracted from 10.
4 The higher the value, the more serious the degree of brain drain is.

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The second brain drain proxy data are from the U.S. National Science Foundation's ‘Doctorate
Recipients from U.S. Universities’ dataset, which can be downloaded from NSF website 5. More
specifically, the data we used are taken from the ‘Share of doctorate recipients with temporary
visas intending to stay in the U.S. after doctorate receipt’. In this paper, we will refer all of the
doctorate degrees in this data as ‘Doctor of Philosophy (Ph.D.)’, though we are aware that not all
doctorate-level degrees are Ph.D. degrees. Thus, we will call this variable as ‘Share of Ph.D.’s
staying in the U.S.’ for convenience. The variable takes a value of zero to one, and covers data
of U.S. Ph.D. recipients from a total of 41 countries over the world with the seven-year time series
availability from 2010 to 2016. The U.S. universities’ Ph.D. graduates, who are considered to be
the frontiers of advanced technology, can be classified as the ‘top-class’ professionals in their
fields, and therefore, we postulate that the higher the percentage of them remaining in the U.S.
is, the higher the level of brain drain their home countries face.
4.1. IMD Brain Drain Index data
First, Table 2 of IMD brain drain index rankings by country shows that Hungary, Venezuela, and
Bulgaria are ranked the first, second and the third as of 2016. Not surprisingly, the upper-ranked
countries are mostly middle-income or lower-middle-income countries, rather than the richest or
poorest countries. As described in the table above, seven out of the top 10 countries, i.e., Hungary,
Bulgaria, Ukraine, Croatia, Slovakia, Slovenia, and Russia, are Eastern European countries. This
is likely to be the result of the recent increase in joins of these East European countries into the
European Union, acquiring domestic labor’s freedom of movement and employment in advanced
countries such as U.K., Germany, and France.
We chose four countries, South Korea, China, Japan, and U.S. as samples to review time-
series trends of individual economies. We chose China because it is one of the fast-growing
countries with largest population size in the world, U.S. because of its largest GDP and highest
technology level with top-class quality of human capital, South Korea because it is known as a
miraculous example case where education and human capital played important roles to escape
from the ‘middle-income trap’ and achieve sustained rapid growth, i.e., ‘six-percent six-decade’
growth. Finally, Japan was selected because it is widely known that it has become a trend in
Japan that Japanese students do not pursue doctorate program abroad, and at the same time,
the economy has been slowing down for decades despite its large size.
5 https://www.nsf.gov/statistics/doctorates/

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Figure 1. Time series of IMD brain drain index of South Korea, China, Japan, and U.S.A. Source: IMD World
Competitiveness Executive Opinion Survey
Figure 1 shows that China’s IMD brain drain index has been somewhat flat with some up and
downs, but decreasing since 2013, while that of Japan has been rising. Moreover, in recent years,
South Korea’s IMD brain drain index exceeded that of China (in years 2012, 2014, 2015, and
2016). In 2016 South Korea was ranked 16th at 6.40, surpassing China’s 6.05 (20th). Also, it is
interesting to notice that South Korea’s brain drain has leaped dramatically during the global
financial crisis, in years 2007 and 2008, and since then, the trend is more or less flat, aside from
the one-time negative shock in 2013.
4.2. NSF Doctorate Recipients from U.S. Universities data
Next, Table 3, the 2016 U.S. NSF’s ranking of the share of Ph.D. recipients staying in the U.S.
after their completion in the course, tells that Iran, Bangladesh and Venezuela are at the top of
the list. Unlike the IMD brain drain index, six of the top 10 countries are made up of Asian countries,
Iran, Bangladesh, Nepal, India, China, and Taiwan, while only two Eastern European countries,
Bulgaria and Romania, are listed in the top 10. Also, Venezuela, a South American country, and
Nigeria, an African country, are ranked third and eighth, respectively. This is attributed to the
absence of the effect of Eastern European countries’ membership status in the European Union.
In other words, there are no visa problems for U.S. Ph.D. holders from Eastern Europe who have
gone to settle down in the advanced European countries such as U.K. and Germany, and they
are not counted and not reflected in this ranking. This is because the top universities and research
institutes in Europe do not lag far behind compared to those of the U.S. in terms of research
environment and the quality of co-workers.
Global Financial Crisis
12 3 4 5 6 7 8
IMD Brain Drain Index (10 - original value)
1995
1998
2001
2004
2007
2010
2013
2016
Year
South Korea China Japan USA

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Table 3. Rankings of Share of new Ph.D.’s Staying in the U.S. by Country in 20166.
Rank Country
No. of US
PhD
Recipients
Share of
Staying in
the US
Rank Country
No. of US
PhD
Recipients
Share of
Staying in
the US
1 Iran 695 0.911 22 Italy 167 0.659
2 Bangladesh 185 0.903 23 Kenya 61 0.656
3 Venezuela 27 0.889 24 Argentina 63 0.635
4 Bulgaria 44 0.886 25 Spain 73 0.616
5 Nepal 226 0.885 26 Columbia 187 0.615
6 Romania 51 0.882 27 Mexico 222 0.613
7 India 2,203 0.872 28 Germany 183 0.612
8 Nigeria 111 0.847 29 Turkey 472 0.576
9 China 5,534 0.809 30 Brazil 155 0.568
10 Taiwan 593 0.779 31 Canada 407 0.565
11 Philippines 51 0.765 32 Britain 115 0.548
12 Vietnam 124 0.718 33 Japan 166 0.530
13 Greece 84 0.714 34 Israel 83 0.518
14 Ukraine 31 0.710 35 Pakistan 102 0.451
15 Lebanon 58 0.707 36 Jordan 98 0.449
16 Ghana 87 0.701 37 Indonesia 92 0.435
17 Russia 108 0.694 38 Singapore 90 0.389
18
South
Korea
1,228 0.668 39 Chile 130 0.323
19 Australia 42 0.667 40 Thailand 185 0.303
20 France 105 0.667 41
Saudi
Arabia
238 0.101
21 Egypt 118 0.661
Note. Source: U.S. National Science Foundation
Interestingly, Figure 2 shows a different pattern in the number of Ph.D.’s between South Korea
and China. Korea’s share of doctorate recipients staying in the U.S. sharply increases since 2013
while that of China continuously drops since 2012. Moreover, the number of new Ph.D. graduates
from Korea decreases since 2012, while that of China has risen continuously and significantly at
least from the year 2010, showing the seemingly opposite trends. In Korea, a high percentage
(52.4%) of doctorate holders are starting their research career as non-regular workers (Song et
al., 2016)7, which are often discriminated against not only by the threat of job security but also by
limitations to research activities, wages, children’s education supports, and welfare systems
compared to the full-time positioned workers.
On the other hand, since the 1990s, Chinese government started an initiative called ‘Thousand
Talents Plan (TTP)’ or so-called ‘The Salmon Project’, of which the goal is to bring back 1000
talents to China who have studied in advanced countries by providing exceptional benefits
6 The higher the value, the more serious the degree of brain drain is.
7 In 2016, 75.5% of South Korea’s new doctorate recipients are employed or confirmed to be employed, and among
them, only 63.1% are employed for full-time regular positions, and 36.9% are employed for temporary (non-regular)
positions. In sum, more than half (52.4%) of South Korea’s doctorate recipients are either unemployed or employed for
temporary jobs.

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including financial reward of one million yuan per person, housings, medical insurance, and even
the high-quality education opportunities for their children. (Oh, 2016).
This plan, which is also translated as the ‘The Recruiting Program of Global Experts’, aims to
recruit world-class scholars, entrepreneurs, professional technicians and manager-level talents
over the next five to ten years and deploy them to state-driven research projects, state-owned
enterprises and banks, and industrial technology complexes (Lee, 2018). As a result of these
efforts, many Chinese doctorate recipients appear to be returning to their home countries to settle
down, and graduate students actively seek for Ph.D. study opportunities in the U.S. because they
are aware of these rewards.
Figure 2. Number of U.S. Ph.D. recipients and the share of them intending to stay in the U.S. Source: U.S.
National Science Foundation
On the other hand, for Japan and Iran, the number of Ph.D. recipients and the share of Ph.D.’s
staying in the U.S. move in the same direction. In Japan, both the number of Ph.D. recipients and
the share of Ph.D.’s staying in the U.S. show downward patterns by 2015. In the case of Iran,
both variables show a pattern of upward movement, and the gradients are steep. In particular, the
number of U.S. doctorate recipients from Iran grows rapidly from 147 in year 2010 to 695 in 2016.
Iran was selected as a new sample for analysis simply because its ‘share of Ph.D.’s staying in
the U.S.’ was the highest among the sample countries as of 2016.
1200 1300 1400 1500
.58 .6 .62 .64 .66
2010 2012 2014 2016
South Korea
3500 4000 4500 5000 5500
.81 .815 .82 .825
2010 2012 2014 2016
China
160 180 200 220 240
.4 .45 .5 .55
2010 2012 2014 2016
Year
Japan
0200 400 600 800
.84 .86 .88 .9 .92
2010 2012 2014 2016
Year
Iran
- No. of New Ph.D.s (Straight)
- Share of Staying (Dotted)

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Table 4. Pearson correlation analysis between brain drain index and selected macroeconomic variable.
Variables8
Correlation
Coefficient
P-value
Brain Drain Index Share of Staying in the US 0.519 0.000
Brain Drain Index Total Factor Productivity -0.477 0.000
Brain Drain Index GDP per Capita -0.452 0.000
Brain Drain Index Human Capital Index -0.271 0.000
Share of Staying in the US Total Factor Productivity -0.283 0.000
Share of Staying in the US GDP per Capita -0.415 0.000
Share of Staying in the US Human Capital Index -0.255 0.000
4.3. Correlation Analysis of Brain Drain Indicators
Table 4 shows the results of Pearson correlation tests between the brain drain variables and key
macroeconomic variables. We are presenting this correlation analysis to show the landscape of
macroeconomic data and their relationships with brain drain variables before moving on to the
regression analysis.
First, there was a strong correlation between IMD brain drain index and the share of Ph.D.
recipients staying in the U.S. The higher the share of Ph.D.’s staying in the U.S. is, the higher the
brain drain index is. In addition, both variables have negative correlation with the key
macroeconomic growth variables, i.e., total factor productivity, per capita GDP, and human capital
index. The more technically advanced, or the more productive the countries are, the richer the
countries are, and the higher the human capital index is, the less brain drain there is.
5. Empirical analysis
5.1. Model Specification
In this study, we use these two variables described earlier, IMD’s ‘brain drain index’ and NSF’s
‘share of Ph.D.’s staying in the U.S.’ as key explanatory variables and explain the economic
growth and total factor productivity growth using fixed-effects linear regression models. The
growth regression models are represented by the following six reduced-form equations such that:
 1: _,~+5 =0+1,+,+++. [3]
 2: _,~+5 =0+1,+,+++. [4]
 3: _,~+5 =0+1,+0,+1,+++. [5]
8 Total factor productivity index is ‘ctfp’ from Penn World Table (PWT) 9.1 (Feenstra RC, Inklaar R & Timmer MP, 2015),
GDP per capita is ‘rgdpe/pop’ from PWT, human capital index is ‘hc’ fr o m P W T.

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 4: _,~+5 =0+1,+,+++. [6]
 5: _,~+5 =0+1,+,+++. [7]
 6: _,~+5 =0+1,+0,+1,+++. [8]
where _,~+5 and _,~+5 are average annual growth rates of home country  ’s income
(per-capita GDP) and total factor productivity from year  to year + 5, respectively. , and
, denote country ’s Brain Drain Index and share of doctoral graduates staying in the U.S.
at year , respectively. , represents the number of U.S. Ph.D. recipients of country  in
year . This term is included to control for the differences in capacities of sample countries to
send their students for U.S. doctoral programs. , is a set of external environmental and policy
variables that may affect the dependent variables, namely, lagged income (lagged GDP per
capita), years of schooling, fertility rate, investment to GDP ratio, government spending to GDP
ratio, trade openness, and terms of trade change. These variables are often used in literature with
conventional growth regression models such as Barro (2016) and Lee (2017). Moreover,  and
 are included to control for the year- and country-specific fixed effects, respectively. .
represents the error term.
The followings are summary statistics of variables used in regression analysis. Table 5 is a
summary statistics table for the regression models that use IMD brain drain data, and Table 6 is
the table for the models that use NSF Doctorate Recipients from U.S. Universities data. Since
IMD data contains more country samples with a longer time span, the regression models that use
this data have more observations than those that use NSF data. The correlation matrices of all
used variables are presented in the appendix (see Table A1 and Table A2).
Table 5. Summary Statistics for Model 1 and Model 4 (1993~2017, 61 Countries).9
Variable Obs. Mean Std. Dev. Min Max
GDP per Capita Growth* 1,342 0.034 0.035 -0.133 0.230
TFP Growth* 1,320 0.003 0.028 -0.146 0.125
Brain drain index 1,361 4.80 1.52 1.33 8.56
Lagged income** (USD) 1,525 23,589 17,852 1,170 136,890
Years of schooling 1,464 9.95 2.20 3.29 13.55
Fertility rate 1,500 1.90 0.64 0.93 4.93
Investment/GDP 1,525 0.248 0.069 0.002 0.640
Government spending/GDP 1,525 0.182 0.064 0.037 0.423
Trade openness 1,525 0.800 0.693 0.068 5.457
Terms of trade change 1,525 0.005 0.064 -0.060 1.789
*: Average annual growth rate from year t to year t+5. **: Value of income for year t-5
9 GDP per capita (or income) is ‘rgdpe/pop’ from PWT, years of schooling is ‘yr_sch’ from PWT, investment to GDP ratio
is ‘csh_i’ from PWT, government spending to GDP ratio is from ‘csh_g’ from PWT, trade to GDP ratio is ‘csh_x-csh_m’
from PWT, the terms of trade is ‘pl_x/pl_m’ from PWT, and fertility rate is from World Development Indicator’s ‘Fertility
Rate’ variable.

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Table 6. Summary Statistics for Models 2, 3, 5, and 6 (2008~2017, 41 Countries)
Variable Obs. Mean Std. Dev. Min Max
GDP per Capita Growth* 287 0.022 0.033 -0.133 0.101
TFP Growth* 245 -0.002 0.027 -0.146 0.056
Share of staying in US 410 0.626 0.160 0.122 0.904
Number of US Ph.D. recipients 410 333 780 28 5,454
Lagged income** 410 18,570 14,789 1271 72,583
Years of schooling 360 9.11 2.69 3.11 13.55
Fertility rate 400 2.23 0.96 1.19 5.90
Investment/GDP 410 0.238 0.075 0.076 0.501
Government spending/GDP 410 0.165 0.047 0.059 0.336
Trade openness 410 0.528 0.451 0.084 3.251
Terms of trade change 410 0.013 0.122 -0.042 1.789
*: Average annual growth rate from year t to year t+5
**: Value of income for year t-5
5.2. Results
Table 7 shows the results of linear regression analysis that explain the growth rate of GDP per
capita and the growth rate of total factor productivity. All variables take five-year (plus and minus
2 years) moving average forms to rule out the short-term shocks and capture the long-term trend.
As mentioned above, the dependent variables, GDP per capita growth and TFP growth, are
generated by calculating growth rates from year  to year + 5 to see the future (five-year-later)
effects of brain drain. The control variable ‘Ln (Lagged Income)’ took the lagged form, which uses
the value of the year five years ahead ( − 5).
The analysis shows that both the brain drain index and the share of Ph.D.’s staying in the U.S.
have significant and positive effects on economic growth and productivity growth. Models that
have TFP growth as the dependent variable (models 4, 5, and 6) show greater regression
coefficients for key explanatory variables than the models with GDP per capita growth. This
implies that brain drain more affects TFP growth and suggest that the main channel of brain drain
affecting economic growth is likely to be the productivity growth. In other words, since brain drain
does not directly affect income growth, but via TFP growth, the regression coefficient on TFP turns
out to be stronger, and this is on the same line with our hypothesis. Also, the differences of fit (R-
squared and adjusted R-squared) are higher for models with ‘share of staying in U.S.’ variable
(models 2, 3, 5 and 6), implying that these models better explain the effects of brain drain
compared to other models. In addition, the inclusion of controls for the number of U.S. Ph.D.
recipients does not alter the regression result, suggesting that the possible sample selection
issues related to the magnitude of doctorate recipients are well-controlled.

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Table 7. Results of linear regression of economic and total productivity growth.
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
GDP per Capita Growth (t+5) TFP Growth (t+5)
Brain Drain Index 0.012**
(0.012) 0.013***
(0.003)
Share of Staying in US 0.062**
(0.015)
0.063**
(0.021) 0.080***
(0.003)
0.082***
(0.005)
Ln (Number of US
Ph.D. Recipients) 0.0004 0.002
Ln (Lagged Income) -0.041*** 0.010 0.010 -0.028*** -0.003 -0.003
Ln (Years of Schooling) -0.033 -0.003 -0.004 -0.015 0.011 0.006
Ln (Fertility Rate) -0.045*** -0.014 -0.014 -0.021* -0.008 -0.006
Ln (Investment/GDP) -0.031*** -0.016 -0.016 0.013** 0.017 0.018
Ln (Gov. Sp. /GDP) -0.003 0.070*** 0.070*** 0.014*** 0.081*** 0.081***
Ln (Trade Openness) 0.031*** -0.073*** -0.073*** 0.030*** -0.046* -0.047*
Terms of Trade Chg. -0.096 -0.257** -0.257** 0.164* -0.072 -0.071
Country & Year FE Included Included Included Included Included Included
Observations 1,156 273 273 1,150 238 238
R-squared 0.406 0.801 0.801 0.308 0.735 0.735
Adjusted R-squared 0.357 0.754 0.753 0.252 0.669 0.667
Note. p < 0.1, ** p < 0.05, *** p < 0.01; Values in parenthesis are p-values; ‘Brain Drain Index’ and ‘Share of Staying in
U.S.’ are in natural logarithm terms.
However, in model 1, investment to GDP ratio shows a strong and negative relationship with
per capita GDP growth rate, and in models 2 and 3, trade openness has strong and negative
regression coefficients, and thus, these models seem to be disqualifying to be established as
benchmark models and show possibilities of needs for other control variables. On the other hand,
the correlation coefficients of models 4, 5, and 6 seem to be consistent with the conventional
growth regression models.
Lastly, the authors are acknowledging the potential weakness of the result that even with the
year-specific fixed effects method, due to the moving average forms of the variables, there may
be a possibility of issues with autocorrelation across time. Also, a more thorough analysis can be
conducted with the inclusion of factors that determines the decision of the talented to study abroad
in the U.S., as well as the decision to stay in the U.S. or return to the home country. For example,
the following factors, which are not considered in this paper’s empirical analysis, may improve the
models: the baseline living conditions of home country, job and income prospects in the U.S.
compared to those of home countries, cultural differences, cost of living in the home countries.

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6. Conclusion
According to our international comparisons of brain drain, the patterns of brain drain vary from
country to country. Particularly, South Korea and China show stark differences. South Korea’s
human resource outflow continuously increases and deepens while China's talent outflow is
showing a marked decline. Korean media often argue that the deepening of this pattern will
reduce the relative quality of South Korea’s human capital, and that of China will continue to
accumulate and surpass Korea’s level soon. In general, middle-income countries rather than high-
income group have more brain drain. Also, emerging economies that grow fast tend to experience
more brain drain.
However, the results of growth regression analysis show that so-called brain drain has a
significant impact on both economic growth and productivity (TFP) growth, implying that brain
drain does not always affect the economy in a negative way. Moreover, brain drain has shown to
have a more positive association with productivity growth, suggesting that the main channel of
brain drain inducing economic growth is likely to be the productivity growth. This can be
considered as evidence of the positive effects of ‘brain circulation’, which is one of the brain drain
phenomenon that settlement of the talents in advanced countries can eventually help improve the
productivity of home country by sharing of advanced technologies and skills around them with
colleagues in motherland.
Therefore, from a policy point of vie w, a two-track strategy is suggested to simultaneously
pursue measures to maximize the improvement of national level of technology, which emphasizes
the utilization of overseas resident talents alongside the brain-attraction policy. In this context,
developing nations should try avoiding the unconditional brain-attraction policy. Furthermore, the
support policies to raise the quality of research institutes and universities to the world-class level,
as well as policies that encourage talents-sharing with global frontier firms will also help improve
the home country’s technological prowess through technology and knowledge diffusion in the long
run, as these policies can attract and embrace skilled overseas brain groups.
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Appendix
Table A1. Correlation Matrix for Variables of Models 1 and 4.
Var1 Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9
Var2 0.667 1.000
(0.000)
Var3 0.136 0.060 1.000
(0.000) (0.041)
Var4
-0.327 -0.219 -0.448 1.000
(0.000) (0.000) (0. 000)
Var5 -0.170 0.039 -0.261 0.427 1.000
(0.000) (0.153) (0. 000) (0.000)
Var6 0.041 0.008 0.163 -0.231 -0.509 1.000
(0.136) (0.779) (0. 000) (0.000) (0.000)
Var7
-0.181 -0.085 -0.353 0.409 0.008 -0.087 1.000
(0.000) (0.002) (0. 000) (0.000) (0.766) (0.001)
Var8 0.268 0.143 0.361 -0.405 0.094 -0.141 -0.413 1.000
(0.000) (0.000) (0. 000) (0.000) (0.000) (0.000) (0.000 )
Var9 -0.022 -0.025 -0.244 0.397 0.266 -0.305 0.391 -0.155 1.000
(0.412) (0.371) (0. 000) (0.000) (0.000) (0.000) (0.000 ) (0.000)
Var10
-0.069 -0.105 0.128 -0.028 -0.035 0.038 -0.017 0.038 -0.045
(0.012) (0.000) (0. 000) (0.283) (0.177) (0.139) (0.503 ) (0.134) (0.077)
Note. Values in parentheses are p-values; Var1: GDP per capita growth; Var2: TFP growth; Var3: IMD brain drain index;
Var4: lagged income; Var5: years of schooling; Var6: fertility rate; Var7: investment to GDP ratio; Var8: government
spending to GDP ratio; Var9: trade openness; Var10: terms of trade change

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Table A2. Correlation Matrix for Variables of Models 2, 3, 5, and 6.
Var1 Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9 Var10
Var2 0.866 1.000
(0.000)
Var3 0.154 0.057 1.000
(0.009) (0.378)
Var4
0.198 0.031 0.251 1.000
(0.001) (0.635) (0. 000)
Var5 -0.084 0.094 -0.400 -0.098 1.000
(0.155) (0.144) (0. 000) (0.048)
Var6 -0.032 0.209 -0.252 -0.112 0.747 1.000
(0.592) (0.001) (0. 000) (0.034) (0.000)
Var7
-0.196 -0.294 0.082 -0.145 -0.487 -0.560 1.000
(0.001) (0.000) (0. 104) (0.004) (0.000) (0.000)
Var8 0.157 0.003 -0.079 0.568 0.222 -0.005 -0.330 1.000
(0.008) (0.967) (0. 112) (0.000) (0.000) (0.930) (0.000 )
Var9 -0.058 0.287 -0.084 -0.156 0.233 0.523 -0.361 -0.222 1.000
(0.328) (0.000) (0. 090) (0.002) (0.000) (0.000) (0.000 ) (0.000)
Var10
0.019 0.042 -0.323 -0.058 0.688 0.488 -0.361 0.320 0.009 1.000
(0.753) (0.512) (0. 000) (0.246) (0.000) (0.000) (0.000 ) (0.000) (0.862)
Var11 -0.034 0.060 0.108 -0.036 -0.020 -0.004 0.002 -0.003 0.072 -0.072
(0.570) (0.349) (0. 029) (0.463) (0.694) (0.934) (0.963 ) (0.948) (0.146) (0.144)
Note. Values in parentheses are p-values; Var1: GDP per capita growth; Var2: TFP growth; Var3: share of staying in
U.S.; Var4: number of U.S. Ph.D. recipients; Var5: lagged income; Var6: years of schooling; Var7: fertility rate; Var8:
investment to GDP ratio; Var9: government spending to GDP ratio; Var10: trade openness; Var11: terms of trade change