50
40
50
Oc
ea
nia
South-Ea
st As
ia
a
ric
Af
10
20
30
60
70
80
90
40
30
20
10
70
60
60
10
60
50
70
80
90
100
110
40
30
10
20
30
20
10
40
50
30
20
10
sia
East A
Key features of the global migration
system include the high concentration
of African migration within the continent (with the exception of Northern
Africa), the ‘closed’ migration system of
the former Soviet Union, and the high spatial
focus of Asian emigration to North America
and the Gulf states.
70
14
0
20
40
30
20
10
10
70
60
90
80
io
Un
t
vie
So
n
10
20
40
30
As
ia
30
40
ut
So
h
100
110
12
0
13
0
60
50
110
0
10
90
80
Europ
e
60
70
80
90
50
2005–10
50
40
Data Sheet
10
20
Global Migration
This circular plot shows all global bilateral migration flows for the five-year
period mid-2005 to mid-2010, classified into a manageable set of ten
world regions.
a
ric
e
Am
in
t
La
30
Migration flows within
and between ten world
regions, in 100,000‘s
A COLLABORATION OF IIASA, VID/ÖAW, WU
North America
r.
Fm
West Asia
Unique estimates of migration flows between the top 50 sending and receiving countries
4
2
2
4
4
6
2
4
2
4
2
4
6
8
ed
t
i
n
U
12
14
16
18
10
8
6
4
2
2
4
6
8
12
10
12
Netherlands
2
Switzerland
2
4
6
8
4
2
4
2
Spain
10
12
14
18
16
14
12
2
4
6
8
108
Italy
10
12
14
2
2
4
6
2
8 0
6
10
50
out
Tick marks indicate a country’s gross migration in 100,000’s (here 4.1 mio in India)
15
5
55
5
40
35
10
15
ut
5
Total immigration to Mexico, coloured by origin country (here small [return] flow from USA)
4
2
2
8
6
4
2
10
18
16
14
12
20
28
26
24
22
desh
Israel
Bahrain
45
20
In dia
10
Total emigration from Mexico, coloured by destination country (here USA)
in
in
Flow from Mexico to USA: no gap at origin, large gap at destination; the width indicates its size
United Stat
es
15
Team at the Wittgenstein Centre for Demography and Global Human Capital (IIASA,VID/ÖAW,WU): Nikola Sander, Guy J. Abel and Ramon Bauer. Contact: Nikola Sander, Vienna Institute of Demography (VID/ÖAW), [email protected]
How to read the plot
o
Credits: circular plots created with Circos (Krzywinski, M. et al. Circos:
an Information Aesthetic for Comparative Genomics. Genome Res, 2009, 19:1639–1645)
tan
5
Origin and destination countries are represented by segments around the circle:
tan
10
Bangla
Pak
ist
Qatar
Syria
Jordan
a
i
s
A
t
s
We
Each country is assigned a colour (Mexico: yellow); flows have the same colour as the origin
This work is licensed under a Creative Commons
Attribution-NonCommercial-ShareAlike 3.0 Unported License.
kis
i
Saud ia
Arab
an
1
16 8
14
12
10
8
6
4
2
2
4
Afg
ha
nis
tan
Iran
10
rab
dA
ite tes
Un Emira
12
1
14 6
1
18 0
2
22 4
2
26
28
2
4
6
8
2
4
6
8
2
4
6
2
4
The large circular plot only shows the top 75% of all flows.
Kuwait
Note: The estimates reflect migration transitions over a five-year interval
and thus cannot be compared to annual movements flow data published
by United Nations and Eurostat.
2
4
For an online visualization of the estimates see
www.global-migration.info
or scan the QR code with your phone.
12
14
S
ou
th
As
ia
Uzb
e
akh
s
40
4
4
Kaz
in
2
2
35
6
4
2
30
10 8
4
Rus
sia
10
25
1 6
1 4
1 2
1
6
20
20 8
2
a
nI di
8
15
264
2 2
2
2
4
20
3028
Portu
gal
Ukra
ine
25
3
36
34
32
30
40 8
ov
iet
Un
ion
6
4
2
out
China
2
Fm
r. S
sia
East A
Hong Kong
France
2
4
6
8
6
4
2
Europe
10
8
6
4
2
Mexico
Japan
m
ny
a
m
r
e
G
10
Malaysia
o
d
g
n
Ki
10
12
esia
h
6
6
4
2
Philippin
es
t
S ou
a
c
i
r
Af
om
2
4
Zim
in g d
6
2
dK
2
4
8
te
6
2
4
6
e
w
b
ba
ni
2
ria
e
Nig
4
2
Gh
an
a
2
Bu
rki
na
Fas
o
Côt
e
12
2
4
6
a
2
4
4
2
2
Af
ric
d’Iv
oir e
Egy
p
t
occo
10
2
4
6
8
52
54
56
58
Mo r
50
42
44
46
48
32
34
36
38
40
Cana
da
United States
30
20
22
24
26
28
12
14
16
18
10
0
2
18
16
14
12
10 8
6
4
2
4
6
8
ico
zil
tna
m
ral
ia
The circular plot shows the estimates of directional flows between the
North Ame
50 countries that send and/or receive at least 0.5% of the
rica
world’s migrants in 2005-10. Tick marks indicate
gross migration (in + out) in 100,000’s.
2
South
-Eas
t As
ia
Me x
Br a
Au
st
d
Mya
nm
ar
Sing
apor
e
Indon
u
Per
d
lan
Zea
Tha
ilan
w
V ie
a
i
n
Ne
Oc
ea
The bilateral flows between 196 countries are estimated from sequential stock tables (see overleaf for details). They are comparable across countries and capture the number
a
c
i
r
e
of people who changed their country of
m
A
residence between mid-2005 and
in
t
mid-2010.
La
U
The global intensity of migration
The 20 largest country-to-country flows in 2005–10
Our flow estimates suggest a stable intensity of global migration, with just over 0.6 per cent
of the world population moving over five year periods, 1990-95 to 2005-10.
Visualizing the 20 largest flows in the world in a circular layout and arranging origins and destinations by each country’s mean years of schooling* reveals a remarkably consistent
pattern of migration to countries with higher education levels. The size of the flow is not proportional to the difference in education level.
2000-05
2005-10
Ban
glad
12
esh
Migration to, from and within ten world regions in 2005–10
16
20
2
16
28
32
sia
2
8
4
Rus
2
28
The table shows the intensities of migration to, from and within ten major world regions in
millions. In absolute terms, Europe was the biggest receiver of migrants (8.9 million over five
years), while South Asia was the biggest sender, with 8.7 million emigrants. In Africa and the
former Soviet Union, emigration intensities were lower than within-region flows.
24
20
16
India
12
8
2
United Kingdom
2
4
Tha
8
a
4
2
Philip
di A
rab
i
a
Sp
ain
2
4
South Africa
co
b
Ara
ted s
Uni mirate
E
Mexi
12
one
sia
Sau
8
16
20
Ind
4
2
4
in
Ch
8
d
ilan
Malaysia
pines
16
Mya
2
2
12
r
nma
Qatar
0.14
3.63
2.64
1.98
0.99
1.15
0.53
1.42
0.21
0.64
2
6.06
-3.09
8.21
-0.34
5.90
-8.70
-1.45
-2.51
1.13
-5.23
Zimbabwe
1.58
3.49
0.70
0.67
0.83
8.72
1.97
3.11
0.09
5.46
A
Hong Kong S
8
2
7.64
0.41
8.92
0.33
6.73
0.02
0.52
0.60
1.22
0.23
R
2
4
Moving into the region Moving out of the region Net migration by region Moving within the region
North America
Africa
Europe
Frm. Soviet Union
West Asia
South Asia
East Asia
South-East Asia
Oceania
Latin America
stan
Kazakh
2
4
Region
e
apor
Sing
* Estimates of adult mean years of schooling provided by Wittgenstein Centre Data Lab.
11 Philippines → United States
Flow, in
1000
384
1083
12 Zimbabwe → South Africa
373
Bangladesh → India
618
13 Myanmar → Thailand
314
4
China → United States
546
14 India → Qatar
311
5
Bangladesh → United Arab Emir.
536
15 Pakistan → Saudi Arabia
289
6
Bangladesh → Saudi Arabia
527
16 India → United Kingdom
283
7
India → United States
502
17 Morocco → Spain
273
8
Indonesia → Malaysia
489
18 Kazakhstan → Russia
258
9
Pakistan → United Arab Emirates
437
19 Côte d'Ivoire → Burkina Faso
241
389
20 China → Hong Kong SAR
238
Flow, in
1000
1845
Origin → Destination
1
Mexico → United States
2
India → United Arab Emirates
3
10 Malaysia → Singapore
Rank
1995-00
It appears that most of the largest flows originated in Asia and went to the oil-rich
Gulf countries and the United States. Exceptions to this trend are the flow from Mexico to the United States and flows within Africa (Côte d’Ivoire to Burkina Faso and
Zimbabwe to South Africa). Malaysia and India were the only countries to be both
receivers and senders of very large flows, highlighting the strong effect that migration and differentials in education levels have on the redistribution of population.
Rank
1990-95
24
2005-10
2
2000-05
2
1995-00
roc
co
4
1990-95
0.0
n
Mo
5
0
sta
0.2
10
ire
i
Pak
15
g
d’Ivo
0.4
20
Sta
tes
Côte
25
Uni
ted
0.6
30
8
35
The circular plot depicts the 20 largest country-to-country flows (in absolute
terms) in 2005-10. The origins and destinations of these flows are arranged by
level of education, with Burkina Faso having the lowest mean years of schooling
and the United States the highest. Tick marks indicate the size of the migration
flow in 100,000 increments. Flows have the same colour as the origin country.
yeaHIGH
rs ES
of T
sc me
ho a
ol n
in
so
na Fa
Burki
eanling
m
o
T
ES scho
W
LO rs of
a
ye
12
Percentage of population
0.8
4
Migrants in millions
40
Origin → Destination
Estimating a unique set of global bilateral migration flows
Why estimates and UN flow data are incomparable
International moves are typically enumerated using either a
measurement of migrant stocks or migration flows. A migrant
stock is defined as the total number of international migrants
present in a given country at a particular point in time. A migration flow is defined as the number of people arriving or leaving
a given country during a specific period of time. Flow measures
reflect the dynamics of the migration process.
Official international migration data collected by national statistics institutes, and collated by Eurostat and the United Nations, are not directly comparable due to differences in definitions, measurements and
data collection procedures. In contrast, our estimates of migration flows
between two sequential migrant stock tables capture the number of
people who permanently change their country of residence over five
year periods.
As migration flow data is often incomplete and not comparable across nations, we estimate the number of movements by
linking changes in migrant stock data over time. Using statistical
missing data methods, estimates of the five-year migrant flows
that are required to meet differences in migrant stock totals are
produced. For example, if the number of foreign-born in the
United States increases between two time periods, the minimum migrant flows between the US and all other countries in
the world that are required to meet this increase are estimated.
In the hypothetical example shown in Figure 1, the location of
people born in Country A is given in 2005 and 2010. As we assume no births and deaths in this example, the stock of migrants
across all (of the possible 3) locations in both years are equal
(270 + 30 + 50 = 210 + 80 + 60 = 350).
The number of people born in Country A and living in Country A (blue field) decreases from 270 in 2005 to 210 in 2010.
The number of people born in A and living in Country B (green
field) increases from 30 to 80 and the number of people living
in Country C (orange field) also increases from 50 to 60.
2005
2010
moved
Country A 270
stayed
210
210
50
80
Country B 30
Country C 50
30
10
50
60
Fig. 1: Hypothetical location of people born in Country A
We estimate the minimum number of migrant flows required
to match the differences in the stocks of people born in Country
A. In doing so, we set the number of “stayers”, those who remain in their country of residence between 2005 and 2010 as
the maximum possible number. In this simplified example, 210
people born in A stay in A, 30 stay in B and 50 stay in C. This assumption generates 50 moves from Country A to Country B and
10 moves from Country A to Country C, whilst maintaining the
observed stocks in 2005 and 2010. This estimation procedure
is replicated simultaneously for all 196 countries to estimate
birthplace-specific flow tables, resulting in a comparable set of
global migration flow estimates.
Alterations are made to the original migrant stock counts to
control for births and deaths during the period, using standard
demographic procedures. These alterations allow our countryspecific net migration flows to closely match the net migration
flows published by the United Nations.
Further reading:
Abel, Guy J. 2013. Estimating global migration flow tables using place of birth data. Demographic Research 28 (18): 505546.
It is tempting to evaluate our estimates against official data by dividing
our five-year flows by a factor of five to derive an annual number similar
to that of official data. However, this is not a suitable comparison as the
two measures capture different types of moves.
Annual flow data sourced from administrative records or national surveys capture every move during the reference period, providing the
duration of stay exceeds 12 months (the time criterion differs across
countries). Our five-year flow estimates capture migrants who changed
their country of residence between mid-2005 and mid-2010. Figure
2 depicts the types of movements between three hypothetical countries that can be distinguished for people born in Country A. First, initial
moves (a) involve people moving out of their country of birth; second,
return moves (b) toward their country of birth; and third, onward moves
(c) to a third country.
Our estimates do not distinghish return moves (d) from those who
stayed in Country C. They also cannot identify multiple moves (e) during the interval, where only one transition over the length of the period
is captured. Since the ratio between one-year and five-year migration
numbers differs across countries, depending on how much circular and
return movement occurs, there is no simple algebraic solution to comparing annual register data and our five-year transitions flows
2010
2005
(a)
Country A
Country A
(e)
Country B
(b)
Country B
(c)
(d)
Country C
Country C
Fig. 2: Types of flows distinguished in our estimates
using a hypothetical example for people born in Country A
Immigration (in), emigration (out) and net migration flows for 196 countries in 2005–10 (in 1,000s)
The estimates capture the number of people who permanently changed their country of residence over the five-year period 2005 to 2010 and thus reflect movements over a longer time period than currently published statistics.
Country
EUROPE
Albania
Austria
Belarus
Belgium
Bosnia & Herzegovina
Bulgaria
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Latvia
Lithuania
Luxembourg
Macedonia
Malta
Moldova
Montenegro
Netherlands
Norway
Poland
Portugal
Romania
Russia
Serbia
Slovakia
Slovenia
Spain
Sweden
Switzerland
Turkey
In
Out
Net
31
214
60
215
20
34
37
45
241
109
4
73
752
1330
212
84
13
167
2007
0
0
43
18
5
7
18
297
171
93
316
42
1409
175
37
24
2412
318
306
112
79
54
110
15
30
84
27
1
0
19
4
0
251
780
58
9
2
67
8
10
36
0
16
0
179
20
247
0
38
166
142
273
175
0
2
162
53
123
161
-48
160
-51
200
-10
-50
10
44
240
90
0
72
500
550
154
75
10
100
1999
-10
-36
42
2
5
-172
-3
50
171
55
150
-100
1135
0
36
22
2250
265
182
-49
Country
Ukraine
United Kingdom
AMERICA
Argentina
Aruba
Bahamas
Barbados
Belize
Bolivia
Brazil
Canada
Chile
Colombia
Costa Rica
Cuba
Dominican Republic
Ecuador
El Salvador
French Guiana
Grenada
Guadeloupe
Guatemala
Guyana
Haiti
Honduras
Jamaica
Martinique
Mexico
Netherlands Antilles
Nicaragua
Panama
Paraguay
Peru
Puerto Rico
Saint Lucia
Saint Vincent & Grenadines
Suriname
Trinidad and Tobago
United States
Uruguay
In
386
1722
Out
426
700
Net
-41
1021
74
4
6
2
6
28
5
1392
101
20
119
0
65
139
3
9
0
2
5
3
1
1
2
2
123
11
0
28
6
0
1
1
0
1
1
6391
3
273
0
0
2
7
193
506
293
71
139
43
190
205
259
295
3
5
5
205
43
241
101
102
4
1926
3
200
17
46
724
146
2
5
6
20
1431
53
-200
4
6
-1
-1
-165
-502
1098
30
-120
75
-191
-140
-120
-292
6
-5
-4
-200
-40
-240
-100
-100
-2
-1803
8
-200
11
-40
-725
-146
-1
-5
-5
-20
4959
-50
Country
Venezuela
Virgin Islands
AFRICA
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Côte d'Ivoire
Congo DR
Djibouti
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Libya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mayotte
Morocco
Mozambique
Namibia
Niger
Team at the Wittgenstein Centre for Demography and Global Human Capital (IIASA,VID/ÖAW,WU): Nikola Sander, Guy J. Abel and Ramon Bauer. Contact: Nikola Sander, Vienna Institute of Demography (VID/ÖAW), [email protected]
In
111
0
Out
71
3
Net
40
-4
55
83
79
38
263
370
35
3
39
74
0
206
72
2
50
20
56
0
35
25
263
3
8
80
1
322
32
2
19
16
21
10
3
2
119
19
31
195
0
28
19
387
0
53
20
34
149
10
565
94
2
393
0
0
296
30
38
312
302
18
268
21
21
52
8
38
116
10
10
3
676
138
21
58
-140
82
50
18
-124
370
-18
-18
5
-75
-10
-359
-22
0
-343
20
55
-297
5
-14
-50
-300
-10
-188
-20
300
-21
-6
-20
-100
10
0
-1
-675
-20
-2
-27
Country
Nigeria
Republic of Congo
Réunion
Rwanda
Sao Tome & Principe
Senegal
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Tunisia
Uganda
Western Sahara
Zambia
Zimbabwe
ASIA
Afghanistan
Armenia
Azerbaijan
Bahrain
Bangladesh
Bhutan
Brunei
Cambodia
China
East Timor
Georgia
Hong Kong SAR
India
Indonesia
Iran
Iraq
Israel
Japan
Jordan
Kazakhstan
Kuwait
In
150
50
3
62
0
19
75
0
799
199
11
67
12
9
12
47
42
0
Out
435
0
3
47
7
151
14
299
98
62
17
366
17
28
146
0
126
899
Net
-286
50
0
15
-7
-133
60
-300
701
137
-6
-299
-5
-20
-134
47
-85
-900
13
19
67
447
18
19
49
0
127
0
1
332
709
0
291
0
364
440
380
343
400
392
94
13
0
2918
2
46
254
2021
49
151
156
3632
1276
474
149
90
170
177
335
123
-379
-75
53
447
-2900
16
3
-255
-1895
-50
-150
176
-2924
-1277
-184
-149
273
269
203
7
277
Country
Kyrgyzstan
Laos
Lebanon
Macao SAR
Malaysia
Maldives
Mongolia
Myanmar
Nepal
North Korea
Oman
Pakistan
Palestine
Philippines
Qatar
Saudi Arabia
Singapore
South Korea
Sri Lanka
Syria
Tajikistan
Thailand
Turkmenistan
United Arab Emirates
Uzbekistan
Vietnam
Yemen
OCEANIA
Australia
Fiji
French Polynesia
Guam
Micronesia
New Caledonia
New Zealand
Papua New Guinea
Samoa
Solomon Islands
Tonga
Vanuatu
In
0
0
87
55
696
0
0
0
81
19
184
33
0
30
862
1287
721
80
1
397
0
508
2
3077
7
19
77
Out
132
75
99
4
610
0
15
498
179
22
31
2022
89
1260
5
230
0
110
250
452
296
15
56
0
525
448
211
Net
-132
-75
-13
50
85
-1
-15
-499
-99
-3
153
-1990
-90
-1230
857
1056
721
-30
-250
-55
-296
493
-55
3076
-519
-430
-134
1164
2
0
6
0
6
247
6
1
0
0
0
39
31
1
6
8
0
182
5
16
0
8
0
1125
-29
-1
0
-9
6
65
0
-16
0
-9
0