brain gain in southeast europe: mission (im)possible? mirjana stankovic, phd milena ristovska, phd c
TRANSCRIPT
BRAIN GAIN IN SOUTHEAST
EUROPE: MISSION (IM)POSSIBLE?
Mirjana Stankovic, PhD
Milena Ristovska, PhD c.
Theory
Brain drain
International transfer of human capital
Large scale migration of highly educated/qualified labor force from developing to developed countries
Theory
Negatively impacts sending country’s human capital accumulation and fiscal revenue
Powerful force in economic development via remittances, trade, FDI, and knowledge transfer
Theory
Size of the country and emigration rate are inversely correlated
Average brain drain rates 7 times higher in
small countries
Highest emigration rates middle-income countries people have both the motive and the financial means to emigrate
Potential benefits
Remittances altruism and loan repayment motive
Return Migration and Brain Circulation
Diaspora Externalities reduce transaction and other information costsfacilitate trade, FDI and technology transfer
between home and host country
SEE countries: Brain Drain: Reasons
Dissolution of the past regimes Weak economic structure Low level of industrial production Low performance results of the educational
system High level of public debt High unemployment level Lack of motivation, commitment and trust Corruption
SEE countries: Brain Drain: Trends
“External” brain drain = Experts leaving the country for better professional fulfilment abroad
“Internal” brain drain = Specialists leaving their professions for better paid jobs in the private and/or informal sector of the economy
Reasons for Brain Drain
Innovation system indicator: Low levels of Gross Expenditure on R&D
in different sectors
Low level of investments in R&D by the private sector, the academia and the public sector.
Developed countries’ private sector is the key innovation catalyst.
In SEE: academia & public sector have higher investments in R&D.
GERD, % of GDP
19971998199920002001200220032004200520062007200820090
0.5
1
1.5
2
2.5
3
3.5
4
Albania
BiH
Croatia
Germany
South Korea
Serbia
Slovenia
Macedonia
Year
Percentage
Developed countries
GERD, private sector %
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
0
10
20
30
40
50
60
70
80
90
Albania
BiH
Croatia
Germany
South Korea
Serbia
Slovenia
Macedonia
Year
Percentage
GERD, academia %
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 20090
10
20
30
40
50
60
70
80
90Albania
BiH
Croatia
Germany
South Korea
Serbia
Slovenia
Macedonia
Year
Percentage
GERD, public sector %
199619971998199920002001200220032004200520062007200820090
10
20
30
40
50
60
70
80
Albania
BiH
Croatia
Germany
South Korea
Serbia
Slovenia
Macedonia
Year
Percentage
Emigration rates by educational level 1995–2005, selected SEE countries
1995 Total
1995 Low
1995 Medium
1995 High
2005 Total
2005 Low
2005 Medium
2005 High
0%
5%
10%
15%
20%
25%
30%World average
Macedonia
Serbia and Montenegro
Croatia
Bosnia and Herzegov-ina
Albania
International skilled migration, estimates controlling for age of entry, percentages
Brain drain0+ years age
Brain drain12+ years age
Brain drain18+ years age
Brain drain22+ years age
1990 2000 1990 2000 1990 2000 1990 2000
Albania 17,4 14,3 17,3 14,1 17,1 13,9 16,1 13,2
Bosnia & Herzegovina
23,9 23,2 22,9 21,9
Macedonia 29,1 26,9 25,9 24,1
Croatia 24,1 22,1 20,7 18,9
Bulgaria 4,0 6,8 3,9 6,6 3,8 6,5 3,7 6,2
Serbia & Montenegro
13,7 13,3 12,9 12,3
Romania 9,1 11,9 8,7 11,4 8,2 10,8 7,7 10,2
Possible solutions
Brain Circulation?
What are the main reasons for highly educated Diaspora to engage in brain circulation?
Human Development Index (HDI) Control of Corruption University-Company Research Collaboration Availability of Venture Capital Patent Applications Granted by the USPTO High-Technology Exports as % of Manufactured Exports Firm-Level Technology Absorption Public Spending on Education as % of GDP Researchers in R&D Brain Drain Difficulty of Hiring Index
University-Company Research Collaboration
(1-7), 2010
Patents Granted by USPTO, avg 2005-2009
High-Tech Exports as % of Manuf. Exports, 2009
Firm-Level Technology Absorption (1-7), 2010
0 1 2 3 4 5 6 7 8 9
Macedonia
Upper Middle In-come
Serbia
Control of Corruption, 2009
Researchers in R&D, 2009
University-Company Research Collaboration (1-7), 2010
Patents Granted by USPTO, avg 2005-2009
High-Tech Exports as % of Manuf. Exports, 2009
Firm-Level Technology Absorption (1-7), 2010
0 1 2 3 4 5 6 7 8 910
Macedonia
High income
Serbia
What can the governments do?
Establishment of industrial clusters linked to science
and university parks
Establishment of innovative start–ups by
entrepreneurial returnees
Promotion of activities by expatriates acting as
“transnational professional communities” between the
sending and the destination country
Brain drain not as a loss, but a potential gain to the home country.
Challenge: building a sustainable brain circulation network.
Adoption of a regional approach to this issue.
Remittances: Do they matter in the context of brain drain?
Do highly educated individuals leave with their families, while cutting their ties with
their home country and investing back very little or not at all?
Determinants of International Remittances Micro-economic level of analysis – Lucas and Stark (1985), Agarwal and
Horowitz (2002), Foster and Rosenzweig (2001), Ilahi and Jafarey (1999).
Migrant workers are motivated to remit for a variety of reasons, ranging from pure altruism to pure self-interest.
Altruistic – migrants’ remittances increase with declines in family income at home
Self-interest motives – remittances are positively related with family income at home.
Macro-economic level of analysis - (El-Sakka & McNabb, 1999; Faini, 1994; Glytsos, 1997; Higgins, Hysenbegasi, & Pozo, 2004).
Macro-economic factors—like interest rates, exchange rates, and political instability—all have an impact on the level of international remittances received by countries. Interest and exchange rates need to be competitive, and that countries need to be politically stable in order to encourage the flow of remittances to labor-sending countries.
Analysis of Remittances Levels in SEE
– Remittances inflows to origin country
– Log of the total number of migrants from origin country
– GDP of origin country measured in PPP terms (in current international $)
– GDP per capita in origin country measured in PPP terms (in current international $)
– Expected or actual rate of growth of GDP (%)
– Degree of development of the financial sector (measured using the ratio of outstanding deposits with commercial banks to GDP - % of GDP)
- Ratio of migrants with a certain level of education (primary, secondary or tertiary) to the total number of migrants
– Error term.
Variable name Description Source
Log of remittancesLog of remittances inflows to origin country (current international $)
Bilateral Remittances Matrices, The World Bank
Log of migrantsLog of total number of migrants in selected OECD countries
IAB brain-drain data, Institute for Employment Research
logGDPLog of GDP expressed in PPP terms (current international $)
World Development Indicators, The World Bank
LogGDP per capitaLog of GDP per capita expressed in PPP terms (current international $)
World Development Indicators, The World Bank
Expected GDP growth Annual GDP growth (%) World Development Indicators, The World Bank
Development of the financial sectorRatio of outstanding deposits with commercial banks to GDP (% of GDP)
Balance of Payments Statistics, IMF
Number of university-level educated migrants to total migrants
Ratio of tertiary educated to total number of migrants (%).
IAB brain-drain data, Institute for Employment Research
Variable Definitions and Sources
Remittances Inflows to SEE from Migrants with Tertiary Education
Regression Statistics
Multiple R 0.866203721
R Square 0.750308886
Adjusted R Square 0.001235545
Standard Error 0.306007932Observations 9
ANOVA df SS MS F Significance F
Regression 6 0.56277185 0.093795308 1.001649431 0.57760354Residual 2 0.187281708 0.093640854Total 8 0.750053558
CoefficientsStandard
Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 6.405622906 11.9016175 0.538214483 0.644312605 -44.80290413 57.61414994 -44.80290413 57.61414994
log(Migration) 0.871362432 2.126319812 0.409798388 0.721678251 -8.277453313 10.02017818 -8.277453313 10.02017818
log(GDP PPP) -0.411774731 1.764206738 -0.233405033 0.837160606 -8.002543668 7.178994205 -8.002543668 7.178994205
log(PCGDP PPP) 0.360180104 4.25167231 0.084714926 0.940204688 -17.93328937 18.65364958 -17.93328937 18.65364958
GDP growth rate -0.004450031 0.124232859 -0.035820077 0.974679501 -0.53898088 0.530080819 -0.53898088 0.530080819
Fin. sector develop. 0.010375523 0.009236946 1.12326341 0.378046053 -0.029367846 0.050118893 -0.029367846 0.050118893
HIGH EDU -0.005723435 0.041935914 -0.136480523 0.903939986 -0.186159109 0.174712238 -0.186159109 0.174712238
Remittances Inflows to SEE from Migrants with Secondary Education
Regression StatisticsMultiple R 0.866403R Square 0.750653Adjusted R Square 0.002614Standard Error 0.305797Observations 9
ANOVA
df SS MS FSignificance
FRegression 6 0.56303 0.093838 1.003494 0.577021Residual 2 0.187023 0.093512Total 8 0.750054
CoefficientsStandard
Error t Stat P-value Lower 95% Upper 95%Lower 95.0%
Upper 95.0%
Intercept 5.35309 5.926693 0.903217 0.461741 -20.1474 30.85359 -20.1474 30.85359log(Migration) 1.158572 0.943708 1.22768 0.344451 -2.90188 5.21902 -2.90188 5.21902log(GDP PPP) -0.658 0.812259 -0.81009 0.50295 -4.15287 2.836862 -4.15287 2.836862log(PCGDP PPP) 0.863063 1.988077 0.434119 0.706546 -7.69094 9.417067 -7.69094 9.417067GDP growth rate 0.008353 0.046591 0.179292 0.874228 -0.19211 0.208819 -0.19211 0.208819Fin. sector develop. 0.01008 0.010126 0.995479 0.424394 -0.03349 0.053649 -0.03349 0.053649MID EDU -0.0034 0.02324 -0.14634 0.897069 -0.10339 0.096592 -0.10339 0.096592
Remittances Inflows to SEE from Migrants with Primary Education
Regression StatisticsMultiple R 0.866536R Square 0.750884Adjusted R Square 0.003536Standard Error 0.305655Observations 9
ANOVA
df SS MS FSignificance
FRegression 6 0.563203 0.093867 1.004732 0.576631Residual 2 0.18685 0.093425Total 8 0.750054
CoefficientsStandard
Error t Stat P-value Lower 95% Upper 95%Lower 95.0%
Upper 95.0%
Intercept 5.614672 6.821816 0.823047 0.497001 -23.7372 34.96658 -23.7372 34.96658log(Migration) 1.040079 1.106762 0.93975 0.446548 -3.72193 5.802089 -3.72193 5.802089log(GDP PPP) -0.55759 0.908055 -0.61405 0.601726 -4.46463 3.349457 -4.46463 3.349457log(PCGDP PPP) 0.647728 2.474884 0.26172 0.818026 -10.0008 11.29629 -10.0008 11.29629GDP growth rate 0.002456 0.072397 0.033925 0.976018 -0.30904 0.313956 -0.30904 0.313956Fin. sector develop. 0.010085 0.009969 1.011651 0.418191 -0.03281 0.052978 -0.03281 0.052978LOW EDU 0.002435 0.015954 0.152602 0.892717 -0.06621 0.07108 -0.06621 0.07108
Main Findings from the Empirical Analysis
The impact of migrants’ education level on remittances is negative and significant at the 5% level. The negative sign of the coefficients implies that migrants with tertiary education remit less than less-educated migrants.
The impact of home countries’ financial sector development is positive, though not significant.
The elasticity of remittances with respect to GDP is negative.
The elasticity of per capita remittances with respect to per capita GDP is positive.
Implications from the Main Findings
An increase in the share of migrants with tertiary education has a negative impact on total and per capita remittances
This contradicts the claim that the negative impact of the brain drain can be mitigated or even offset by the fact that skilled migrants remit more than unskilled ones.
These findings thus provide an additional source of concern about the brain drain for countries of origin. This should raise the urgency of finding (non-distortive) ways to reinforce skilled migrants’ links with their country of origin. This might possibly be achieved as part of a cooperative arrangement between source and (their principal) host countries, including policies of return and circular migration (Schiff, 2007).
