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Dynamics and determinants of emigration: the case of Croatia and the experience of new EU member states
Ivana Draženović*
Ivana Draženović
Affiliation: Center for Economic Research and Graduate Education – Economics Institute, Prague, Czech Republic
 0000-0002-6738-1288
Marina Kunovac* 
Article | Year: 2018 | Pages: 415 - 447 | Volume: 42 | Issue: 4 Received: June 1, 2018 | Accepted: November 4, 2018 | Published online: December 14, 2018
|
FULL ARTICLE
FIGURES & DATA
REFERENCES
CROSSMARK POLICY
METRICS
LICENCING
PDF
Notes: Eastern Croatia encompasses Virovitičko-podravska, Požeško-slavonska, Brodskoposavska, Osječko-baranjska and Vukovarsko-srijemska counties. Central Croatia encompasses Zagrebačka, Sisačko-moslavačka, Karlovačka and Bjelovarsko-bilogorska counties. Lika and Gorski kotar encompass Primorsko-goranska and Ličko-senjska counties. Central and Southern Adriatic encompass Zadarska, Šibensko-kninska, Splitsko-dalmatinska and Dubrovačkoneretvanska counties. Northen Adriatic refers to Istarska County. Northwestern Croatia encompass Krapinsko-zagorska, Varaždinska, Međimurska and Koprivničko-križevačka counties. Source: CBS.
Note: The size of the circles correspond to the emigration rate, as % of total population of the county. Source: CBS.
Note: * Germany and Italy lifted transitional provisions for Croatia in 2015. ** UK and Austria are applying transitional provisions until June 2018, with possible extension until 2020. Source: CBS.
Note: Official Central Bureau of Statistics emigration number for emigration in EU 27. Core EU countries are represented by 11 countries, due to data availability: Austria, Belgium, Denmark, Finland, Germany, Ireland, Italy, Luxembourg, Netherlands, Sweden and United Kingdom, in thousands. Source: CBS, national statistical offices of the core EU countries.
Sources: CBS, national statistical offices and Eurostat; authors’ calculations.
|
Origin Country
|
Top 3 emigration destinations in EU, as % of total EU emigration
|
|
Bulgaria
|
n/a
|
n/a
|
n/a
|
|
Croatia
|
Germany, 71
|
Austria, 8
|
Ireland, 7
|
|
Czech Republic
|
Slovakia, 60
|
Germany, 9
|
Poland, 6
|
|
Estonia
|
Finland, 63
|
United Kingdom, 8
|
Germany, 7
|
|
Hungary
|
Germany, 32
|
Austria, 27
|
United Kingdom, 17
|
|
Latvia
|
n/a
|
n/a
|
n/a
|
|
Lithuania
|
United Kingdom, 60
|
Ireland, 11
|
Germany, 10
|
|
Poland
|
Germany, 43
|
United Kingdom, 28
|
Netherlands, 8
|
|
Romaniaa
|
Spain, 24
|
Germany, 17
|
Italy, 16
|
|
Slovakia
|
Czech Republic, 38
|
Austria, 27
|
Germany, 10
|
|
Slovenia
|
Germany, 27
|
Austria, 27
|
Croatia, 12
|
a Percentage of total emigration. Sources: CBS, national statistical offices and Eurostat.
Note: Dashed lines denote the years of EU accession. Source: National statistical offices of the core EU countries.
Source: National statistical offices of the core EU countries.
Source: Eurostat and national statistical offices of the core EU countries.
|
|
Model
1
(Baseline) FE
|
Model
1
(Baseline) PPML
|
Model
2
FE
|
Model
2
PPML
|
|
Distance
|
-
|
-1.48***
|
-
|
-1.54***
|
|
-
|
(0.00)
|
-
|
(0.00)
|
|
Population
|
0.59
|
1.41
|
0.99**
|
5.85***
|
|
(0.17)
|
(0.35)
|
(0.02)
|
(0.00)
|
|
GDP PC in PPS (origin)
|
0.11
|
0.27
|
|
|
|
(0.59)
|
(0.46)
|
|
|
|
GDP PC in PPS (destination)
|
1.55
|
2.15**
|
|
|
|
(0.00)
|
(0.01)
|
|
|
|
Transitional provisions
|
0.54
|
0.34***
|
0.46***
|
0.46***
|
|
(0.00)
|
(0.00)
|
(0.00)
|
(0.00)
|
|
Employment rate (origin)
|
|
|
-1.45***
|
-5.04***
|
|
|
(0.00)
|
(0.00)
|
|
Employment rate (destination)
|
|
|
1.2*
|
8.15***
|
|
|
(0.06)
|
(0.00)
|
|
Output gap (origin)
|
|
|
-2.27***
|
3.07
|
|
|
(0.00)
|
(0.2)
|
|
Output gap (destination)
|
|
|
3.74***
|
2.03**
|
|
|
(0.00)
|
(0.04)
|
|
Corruption index (origin)
|
|
|
0.03
|
-1.66***
|
|
|
(0.89)
|
(0.00)
|
|
Corruption index (destination)
|
|
|
3.78***
|
2.46*
|
|
|
(0.00)
|
(0.09)
|
|
Share of youth (20-34) origin
|
|
|
1.5***
|
0.19
|
|
|
(0.00)
|
(0.8)
|
|
Share of tertiary educated
(origin)
|
|
|
0.25
|
0.58*
|
|
|
(0.14)
|
(0.07)
|
|
Cons
|
-11.91
|
0.23
|
-13.65
|
5.51
|
|
Number of observations
|
1958
|
1972
|
1958
|
1972
|
|
R2
|
0.46
|
0.78
|
0.53
|
0.82
|
Note: *, ** and *** refer to 10%, 5% and 1% statistical significance levels, respectively. P-values are in parenthesis. All specifications include origin and destination fixed effects dummies. Parameters associated to output gap for origin and destination country are multiplied by 100 since the output gap enters the model specification in levels instead of being transformed into logarithms, due to negative values. Source: Authors’ elaboration based on national statistical offices of the core EU countries immigration data and on the data presented appendix 1.
|
Data Sources and details for set of independent
variables
|
|
Variable
|
Description
|
Source
|
Estimation
details
|
|
GDP PC in PPS
|
Gross domestic product at market prices, current
prices, PPS per capita
|
Eurostat online statistical database
|
Destination and origin country, in log
|
|
Unemployment
rate
|
Yearly unemployment rates, from 15 to 64 years,
percentage
|
Eurostat online statistical database
|
Destination and origin country, in log
|
|
Population
|
Population on 1 January, total
|
Eurostat online statistical database
|
Relative values between destination and origin
country, in log
|
|
Distance
|
Distance between two countries is calculated
based on latitudes and longitudes of the most important cities/agglomerations
(in terms of population). Mayer and Zignago (2011)
|
CEPII database
|
In log
|
|
Youth population number
|
Population on 1 January, from 20 to 34 years
|
Eurostat online statistical database
|
Origin country, as a share in total population,
in log
|
|
Tertiary educated
|
Population by educational attainment level,
from 15 to 64 years, tertiary education (levels 5-8)
|
Eurostat online statistical database
|
Origin country, as a share in total population,
*1000, in log
|
|
Corruption index
|
Control of corruption captures perceptions of
the extent to which public power is exercised for private gain, including both
petty and grand forms of corruption, as well as "capture" of the state
by elites and private interests link
|
Worldwide Governance Indicators (WGI), The World
Bank
|
Destination and origin country, in log
|
|
Governance index
|
Government effectiveness captures perceptions
of the quality of public services, the quality of the civil service and the degree
of its independence from political pressures, the quality of policy formulation
and implementation, and the credibility of the government's commitment to such
policies link
|
Worldwide Governance Indicators (WGI), The World
Bank
|
Destination and origin country, in log
|
|
Output gap
|
Output Gaps (% of potential output), HP filter
|
European Commission CIRCAB, II. autum forecast
|
Destination and origin country
|
|
Employment rates
|
Yearly employment rates, from 15 to 64 years,
percentage
|
Eurostat online statistical database
|
Destination and origin country, in log
|
|
Transitional Provisions
|
Variable representing the access to common free
EU market for BG and RO takes value 1 for FI, SE from 2007, for DK from 2009,
for IT and IE from 2012 and for all other countries from 2014. Variable representing
the access to common free EU market for HR takes value 1 for DK, FI, IR, SE from
2013, for BE, IT, DE, LU from 2015, while NL, AT and UK apply transitional provisions
for HR during the entire sample period (sample is ending in 2016, while transitional
provisions applied by NL, AT and UK should be lifted by June 2018). Variable representing
the access to common free EU market for CZ, SK, SI, PL, HU, LV, LT, EE takes value
1 for UK, SE, IE from 2004, for IT, FI from 2006, for NL, LU from 2007, for BE,
DK from 2009 and for AT, DE from 2011
|
European Commission
|
Set of dummy variables
|
|
Data Sources and details for set of
independent variables
|
|
Variable
|
Description
|
Source
|
Estimation
details
|
|
Emigration flows
|
Data for IR, NL, FI, SE, IT, AT, LU, DK avaliable
on line. Data for DE, BE, UK, obtained on email request. Data for UK and IE refers
to immigration numbers and not to official migration statistics.
|
National Statistical Offices websites of core
EU countries
|
For static models - emigration from origin country
i into destination country j in time t, for dynamic model - share of emigrants
in total population of origin country, in log
|
|
Data for Germany and Denmark are based on country
of previous residence principle. Data for Netherlands, Italy, United Kingdom,
and Belgium on country of birth principle, while data for Sewwden, Finland, Luxemburg
and Austria are based on citizenship principle.
|
|
Core EU countries are represented by 11 countries,
due to data availability: Austria, Belgium, Denmark, Finland, Germany, Ireland,
Italy, Luxemburg, Netherlands, Sweden and United Kingdom. Usually Portugal, Greece,
Portugal, Spain and France are also included in core EU countries. Required immigration
data are not publicaly available on their website. Statistical office of Portugal
delivered the data from our customized request. Since data are starting in 2008
we do not include them in main specifications. Upon conclusion of this paper we
have not managed to receive required data from customized requests sent to other
statistical offices.
|
|
Emigration
from and to Croatia following the EU accession
|
2013
|
2014
|
2015
|
2016
|
2013-2016
|
|
(1) Emigration to core EU countries from national statistical
offices of core EU countries
|
31,655
|
53,666
|
72,528
|
71,314
|
229,163
|
|
(2) Emigration to "rest of the world"
according to CBS
|
11,220
|
9,049
|
11,116
|
9,238
|
40,623
|
|
(3) Total emigration = (1) + (2)
|
42,875
|
62,715
|
83,644
|
80,552
|
269,786
|
|
(4) CNB total emigration
|
15,262
|
20,858
|
29,651
|
36,436
|
102,207
|
|
(5) Emigration coefficient
|
2.8
|
3.0
|
2.8
|
2.2
|
2.6
|
|
(6) Immigration from core EU countries according to national
statistical offices
of core EU countries
|
14,164
|
19,346
|
23,261
|
23,422
|
80,193
|
|
(7) Immigration from "rest of the world"
according to CBS
|
8,676
|
8,540
|
8,512
|
9,705
|
35,433
|
|
(8) Total immigration = (6) + (7)
|
22,840
|
27,886
|
31,773
|
33,127
|
115,626
|
|
(9) CBS total immigration
|
10,378
|
10,638
|
11,706
|
13,985
|
46,707
|
|
(10) Immigration coefficient
|
2.2
|
2.6
|
2.7
|
2.4
|
2.5
|
|
(11) Net emigration = (3) - (8)
|
20,035
|
34,829
|
51,871
|
47,425
|
154,160
|
|
(12) CNB net emigration
|
4,884
|
10,220
|
17,945
|
22,451
|
55,500
|
|
(13) Net emigration coefficient
|
4.1
|
3.4
|
2.9
|
2.1
|
2.8
|
Note: UK and Ireland not included in immigration numbers. Source: CBS and national statistical offices of the core EU countries.
|
|
MoModel
3 Dynamic Model (GMM)
|
|
Distance
|
-0.49***
|
|
(0.00)
|
|
Population
|
0.29
|
|
(0.59)
|
|
Transitional provisions
|
0.25***
|
|
(0.00)
|
|
Employment rate (origin)
|
-2.01***
|
|
(0.00)
|
|
Employment rate (destination)
|
0.53
|
|
(0.47)
|
|
Output gap (origin)
|
3.72
|
|
(0.36)
|
|
Output gap (destination)
|
2.18***
|
|
(0.00)
|
|
Corruption index (origin)
|
-0.37
|
|
(0.40)
|
|
Corruption index (destination)
|
0.57
|
|
(0.55)
|
|
Share of youth (20-34) origin
|
-0.32
|
|
(0.59)
|
|
Share of tertiary educated (origin)
|
0.35
|
|
(0.12)
|
|
ln(m t-1)
|
0.66***
|
|
(0.00)
|
|
Cons
|
7.4
|
Note: *, ** and *** refer to 10%, 5% and 1% statistical significance levels, respectively. P-values are in parenthesis. All specifications include origin and destination fixed effects dummies. Parameters associated to output gap for origin and destination country are multiplied by 100 since the output gap enters the model specification in levels instead of being transformed into logarithms, due to negative values.Source: Authors’ elaboration based on national statistical offices of the core EU countries immigration data and on the data presented in appendix 1.
|
|
Model 4 FE
|
Model 4 PPML
|
|
Distance
|
-
|
-1.52***
|
|
-
|
(0.00)
|
|
Population
|
1.69***
|
6.63***
|
|
(0.00)
|
(0.00)
|
|
Transitional provisions
|
0.47***
|
0.42***
|
|
(0.00)
|
(0.00)
|
|
Unemployment rate (origin)
|
0.19**
|
0.69***
|
|
(0.03)
|
(0.00)
|
|
Unemployment rate (destination)
|
-0.03
|
-1.09***
|
|
(0.66)
|
(0.00)
|
|
Output gap (origin)
|
2.18**
|
1.53
|
|
(0.01)
|
(0.34)
|
|
Output gap (destination)
|
4.64***
|
2.52*
|
|
(0.00)
|
(0.09)
|
|
Governance index (origin)
|
-0.22
|
-2.29***
|
|
(0.52)
|
(0.00)
|
|
Governance index (destination)
|
0.89
|
-2.71
|
|
(0.11)
|
(0.40)
|
|
Share of youth (20-34) origin
|
1.71***
|
1.34
|
|
(0.00)
|
(0.11)
|
|
Share of tertiary educated (origin)
|
0.41**
|
0.69**
|
|
(0.02)
|
(0.01)
|
|
Cons
|
-1.04
|
42.2**
|
|
Number of observations
|
1958
|
1972
|
|
R2
|
0.51
|
0.82
|
Note: *, ** and *** refer to 10%, 5% and 1% statistical significance levels, respectively. P-values are in parenthesis. All specifications include origin and destination fixed effects dummies. Parameters associated to output gap for origin and destination country are multiplied by 100 since the output gap enters the model specification in levels instead of being transformed into logarithms, due to negative values. Source: Authors’ elaboration based on national statistical offices of the core EU countries immigration data and on the data presented in appendix 1.
|
|
2001
|
2002
|
2003
|
2004
|
2005
|
2006
|
2007
|
2008
|
2009
|
2010
|
2011
|
2012
|
2013
|
2014
|
2015
|
2016
|
|
Eastern Croatia
|
0.2
|
0.2
|
0.2
|
0.3
|
0.2
|
0.3
|
0.3
|
0.2
|
0.4
|
0.3
|
0.3
|
0.3
|
0.4
|
0.6
|
1.0
|
1.4
|
|
Central Croatia
|
0.2
|
0.2
|
0.2
|
0.2
|
0.2
|
0.2
|
0.3
|
0.3
|
0.3
|
0.3
|
0.4
|
0.3
|
0.5
|
0.6
|
0.8
|
1.0
|
|
Lika and Gorski kotar
|
0.2
|
0.2
|
0.2
|
0.1
|
0.1
|
0.2
|
0.2
|
0.2
|
0.2
|
0.2
|
0.3
|
0.3
|
0.4
|
0.6
|
0.9
|
1.0
|
|
Central and Southern Adriatic
|
0.1
|
0.2
|
0.1
|
0.1
|
0.1
|
0.1
|
0.2
|
0.2
|
0.2
|
0.3
|
0.4
|
0.4
|
0.5
|
0.5
|
0.6
|
0.7
|
|
Northen Adriatic
|
0.2
|
0.2
|
0.1
|
0.1
|
0.1
|
0.1
|
0.1
|
0.1
|
0.1
|
0.2
|
0.5
|
0.4
|
0.3
|
0.4
|
0.6
|
0.7
|
|
Northwestern Croatia
|
0.1
|
0.1
|
0.1
|
0.1
|
0.0
|
0.1
|
0.1
|
0.0
|
0.0
|
0.0
|
0.1
|
0.1
|
0.1
|
0.3
|
0.4
|
0.6
|
|
City of Zagreb
|
0.2
|
0.6
|
0.2
|
0.1
|
0.1
|
0.1
|
0.1
|
0.1
|
0.1
|
0.2
|
0.3
|
0.3
|
0.3
|
0.4
|
0.6
|
0.6
|
Notes: Eastern Croatia encompasses Virovitičko-podravska, Požeško-slavonska, Brodsko-posavska, Osječko-baranjska and Vukovarsko-srijemska counties. Central Croatia encompasses Zagrebačka, Sisačko-moslavačka, Karlovačka and Bjelovarsko-bilogorska counties. Lika and Gorski kotar encompass Primorsko-goranska and Ličko-senjska counties. Central and Southern Adriatic encompass Zadarska, Šibensko-kninska, Splitsko-dalmatinska and Dubrovačko-neretvanska counties. Northen Adriatic refers to Istarska County. Northwestern Croatia encompass Krapinsko-zagorska, Varaždinska, Međimurska and Koprivničko-križevačka counties. Source: CBS.
Figure 1Net migration balance of Croatia between 2001 and 2016, Central Bureau of Statistics data, net migration DISPLAY Figure
Figure 2Structure of emigrants from Croatia by sex between 2002 and 2016, Central Bureau of Statistics data, gross emigration flows DISPLAY Figure
Figure 3a) Relative share of different age groups of emigrants and average age of emigrant between 2002 and 2016, (b) Number of emigrants by different age groups between 2002 and 2016, Central Bureau of Statistics data, gross emigration flows DISPLAY Figure
Figure 4Structure of emigrants from Croatia by region between 2001 and 2016, Central Bureau of Statistics data, gross emigration flows DISPLAY Figure
Figure 5Unemployment rate and share of emigrants by county in 2016, Central Bureau of Statistics data, gross emigration flows DISPLAY Figure
Figure 6(a) Main EU emigration destinations for Croatians in 2010, (b) Main EU emigration destinations for Croatians in 2016, Central Bureau of Statistics data, gross emigration flows DISPLAY Figure
Figure 7Indirect emigration flows from Croatia to the core EU countries according to national statistical offices of core EU countries, compared to the official emigration numbers to EU 27 countries according to Central Bureau of Statistics, gross emigration flow DISPLAY Figure
Figure 8New MS emigrants’ average age and median age of population, 2000-2016, national statistical offices of NMS countries, gross emigration flows DISPLAY Figure
Table 1Main EU emigration destinations for NMS in 2016 (in % of total EU emigration), national statistical offices of NMS countries, gross emigration flows DISPLAY Table
Figure 9Indirect emigration flows from NMS to the core EU countries, national statistical offices of core EU countries, gross emigration flows DISPLAY Figure
Figure 10Indirect emigration flows of NMS in time, national statistical offices of core EU countries, gross emigration flows DISPLAY Figure
Figure 11Average emigration flow, as % in total population from 2011 to 2016, compared to average unemployment rate (a) and average GDP PC in PPS, (b) national statistical offices of core EU countries, gross emigration flows DISPLAY Figure
Table 2Determinants of emigration flows from new EU Member States to the core EU countries between 2000 and 2016, Fixed effects estimator (FE) and Poisson pseudo maximum likelihood estimator (PPML) DISPLAY Table
Table A1Data sources and details, independent variables DISPLAY Table
Table A2Data sources and details, dependent variable DISPLAY Table
Table A3Total migration flow in Croatia – approximation based on discretional combination of different data sources DISPLAY Table
Table A4Determinants of emigration flows from new EU Member States to the core EU countries between 2000 and 2016, dynamic estimation, Arellano- Bond GMM estimator DISPLAY Table
Table A5Determinants of emigration flows from new EU Member States to the core EU countries between 2000 and 2016, Poisson pseudo maximum likelihood estimator, extended specification Model 4 DISPLAY Table
Table A6Number of emigrants from Croatia by region (as % of total population of the region) between 2001 and 2016. Central Bureau of Statistics data, gross emigration flows DISPLAY Table
* We would like to thank two anonymous referees and the editor for the constructive and very useful remarks. We are particularly grateful to Evan Kraft, Gordi Sušić, Vedran Šošić, Teo Matković, Maja Bukovšak, Nina Ranilović, Davor Kunovac, Marko Mrkalj, Ervin Duraković, Karlo Kotarac and Alan Bobetko for their valuable comments during the early stages of the research. Our work also benefited a lot from the presentation and discussion during the 24th Dubrovnik Economic Conference in June 2018. The views expressed are those of the authors and do not necessarily represent those of the Croatian National Bank.
1 Transitional provisions do not apply on cross boarder movements of citizens for reasons other than work, but only restrict free movement of citizens for work purposes. According to the Accession Treaty for Croatia transitional provisions can apply for a maximum period of seven years (2+3+2 formula). More details are given in Table 3, Appendix 1.
2 Due to data availability, core EU countries are represented by 11 countries: Austria, Belgium, Denmark, Finland, Germany, Ireland, Italy, Luxembourg, Netherlands, Sweden and United Kingdom. New EU Member States are represented by 10 countries: Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia.
3 In addition, several authors implement partial analyses of emigration flows from Croatia following the EU accession. Šonje (  2018) estimates family emigration by using primary school enrolment data and shows that in 2009-2016 period around 50 thousand young citizens with children left Croatia. The Croatian Employment Service uses the annual employers’ survey to examine the extent of migration among the employed, and shows that in 2016 around 20 thousand employed persons emigrated from Croatia. Finally, Jurić ( 2017) did a detailed on line survey among Croatian emigrants in Germany and showed that although economic factors are relevant for emigration decision, there is a prevalence of non-economic factors among the motives of emigration for Croatian emigrants
4 That is 12 or 8 years following EU accession.
5 Estimates are based exclusively on households with children (obtained by comparison of expected and effective primary school enrolment) and are considered to represent irreversible emigration, based on the assumption that child integration in system of destination countries strongly disincentives return-migration.
6 Prolongation of application of transitional provisions in the period from June 2018 until June 2020 is possible only in the case of serious disturbances for the Austrian labour market that would otherwise occur.
7 Illustrative case in point is a Polish example. Following the EU accession Poland experienced a strong emigration flows. At some point policymakers realized that the official statistics grossly underestimate the extent of emigration. As a result, research project has been initiated in Poland in order to properly estimate the true numbers. The upgraded and consolidated sources raised the official emigration numbers by a factor ten (Statistics Poland, 2011).
8 Destination country can register immigrants according to the following principles: country of birth principle, country of previous residence principle and citizenship principle. Registration of immigrants according to the different principles is defined by Eurostat International Migration Statistics.
9 For Ireland personal public service number the principle for registration of immigrants is not denoted.
10 According to the Central Bureau of Statistics, national statistical offices of the selected core EU countries represent broadly around 90% of total emigration to the European Union from Croatia over the entire sample period, which makes them a valid and representative indicator of total emigration flows towards the EU.
11 We have also estimated total emigration flows from Croatia, by putting together (1) indirectly constructed emigration flows to the core EU countries and (2) Central Bureau of Statistics official emigration data for all other emigration destinations, i.e. "the rest of the world". The same approach is followed in order to construct an approximation of total immigration flows in Croatia. Calculation details of total net emigration are given in Appendix 2. According to our discretional combination of different data sources, net emigration from Croatia is estimated to be around 155 thousands person in the 2013-2016 period.
12 At the beginning of 2017 Croatian government adopted the Ordinance for the implementation of the General Tax Act (OG 30/17) that clarified the process of determination of residency status for tax purposes and induced migrants to register their change of residency within authorities to avoid double income taxation.
13 CBS is constantly working on improving migration data sources, so part of the observed developments might reflect underlying methodological changes. For example, in 2011 the CBS changed its definition of migrants from people who registered their departure/arrival to people who are absent from their usual place of residence in a one year period.
14 Nevertheless, there are some peculiarities among main emigration destinations between NMS. Finland was the main destination for emigrants from Estonia, and Spain for emigrants from Romania in 2016, reflecting their cultural and historical linkages.
15 However, all member states but Croatia gained access to the common EU market prior to the onset of the global crisis. Only Croatia joined the EU after six consecutive years of economic distress. This might have created an additional pressure on migration outflows from Croatia. However, proper evaluation of this phenomenon will be possible only with some time delay.
16 Income levels are usually approximated by GDP per capita in PPP terms given that wage data are not comparable across countries.
17 Vukovic ( 2017) shows that the Croatian economy is permeated by corruption since the political system is characterized by systematic corruption, on national and local levels. Also, WGI corruption index data point to a substantial gap in corruption incidence between most NMS and core EU countries in general.
18 As a main alternative to the corruption index we could have used the governance index from the same database. Estimation results obtained with the governance index as independent variable are shown in Appendix 3.
19 We opt for the exclusion of GDP per capita in PPS from the extended model specification since inclusion of GDP PC in PPS and short term economic indicators could result in multicollinearity. Instead, differences in level of economic development are captured by origin and destination fixed effects.
20 A detailed overview of different estimation strategies and models used in assessment of impact of EU accession for CEE countries in 2004 is given in Brücker et al. ( 2009).
21 Changes in the predicted emigration flow for dummy variable representing transitional provisions are calculated according to the formula e^(β_tp )-1.
22 Direct comparison is not possible since the aforementioned authors estimate net migration potential while our analysis is based on gross emigration flows.
23 For more details about relationship between EU membership and labour mobility see Arpaia et al. ( 2016).
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December, 2018
IV/2018
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