Estimating Global Migration Flow Tables Using Place of Birth
Data
Guy J. Abel
¨
Wittgenstein Centre (IIASA, VID/OAW,
WU)
Vienna Institute of Demography/Austrian Academy of Sciences
1
Introduction
International migration flow data often lacks adequate measurements of volumes, direction and
completeness. These pitfalls limit comparative studies of migration and constrain cross national
population projections to use net migration measures or inadequate data. This paper aims to
address these issues at a global level, presenting estimates of bilateral flow tables between
191 countries. A methodology to estimate flow tables of migration transitions for the globe
is illustrated in two parts. First, a methodology to derive flows from sequential stock tables
is developed. Second, the methodology is applied to recently released World Bank migration
¨
stock tables between 1960 and 2000 (Ozden
et al., 2011) estimating a set of four decadal global
migration flow tables. The results of the applied methodology are discussed with reference
to comparable estimates of global net migration flows of the United Nations and models for
international migration flows. The proposed methodology adds to the limited previous literature
on linking migration flows to stocks. The estimated flow tables represent a first-of-a-kind set of
comparable global origin-destination flow data.
2
Data
International migration flow data provided by national statistical offices are not comparable.
Tables of bilateral international migration flow data have severe missing data problems and
inconsistent definitions, see for example Nowok et al. (2006). Efforts to estimate European
migration tables of comparable flow data have been partially successful, see for example Abel
(2010); Beer et al. (2010); Raymer et al. (2012). However, these methodologies rely on a
reasonable percentage of double counted flows, i.e., reported values from both the sending and
receiving counties. The application of these estimation methods to global data does not appear
to be possible, as the availability of reported migration flows from non-European countries
remains scarce.
In comparison to flow data, international migrant stock data is both easier to collect and
compare. The World Bank recently released a global bilateral foreign born migrant stock
¨
database for the last five census rounds (Ozden
et al., 2011). The data is primarily based on
place of birth responses to Census questions or details collected from population registers. In
order to construct a set of complete bilateral tables, issues of definitions, changes in geography,
1
aggregated data and missing values were addressed by the World Bank. The resulting tables
represent the most comparable global data set of past international migration stocks available.
3
Methodology
Bilateral migration data is commonly represented in square tables. Values within the table vary,
depending on definitions used in data collection or the research question at hand. Values in
non-diagonal cells represent some form of movement, for example a migration flow or a foreign
born stock between a specified set of R regions or areas. Values in diagonal cells represent some
form non-moving population, or those that move within a region, and are often not presented.
Table 1: Dummy Example of Place of Birth Migrant Stock Data
A
B
C
D
Sum
Place of Residence (t)
A
B
C
D
Sum
1000
55
80
20
1155
1110
665
960
265
3000
100
555
40
25
720
10
50
800
20
880
0
5
40
200
245
Place of Residence (t + 1)
A
B
C
D Sum
60
75
800
0
935
0
5
40
180
225
1110
665
960
265
3000
Place of Birth=B
Destination
A
B
C
D
Sum
75
5
55
555
50
5
665
Place of Birth=D
Destination
A
B
C
D
Sum
180
20
25
20
200
265
Place of Birth
Place of Birth
Place of Birth Data in Stock Tables:
A
B
C
D
Sum
950
80
90
40
1160
100
505
30
45
680
A
B
C
D
Sum
950
100
60
Origin
Place of Birth=C
Destination
A
B
C
A
B
C
D
Sum
90
30
800
D
Sum
0
1000
100
10
0
1110
D
Sum
40
80
40
800
40
960
Origin
Origin
Place of Birth=A
Destination
A
B
C
Origin
Place of Birth Data in Flow Tables:
2
A
B
C
D
Sum
A
B
C
D
Sum
80
40
505
45
0
Consider two migrant stock tables in consecutive years (t and t + 1) in the top panel of
Table 1. The rows represent places of birth from four regions (A to D). The columns represent
place of residence, which also range from regions A to D. Hence, non-diagonal entries represent
the number of foreign born migrants in each area of residence, whilst diagonal entries contain
the number of native born residents. In this hypothetical data there are no births or deaths.
This results in two noticeable features in the tables. First, the row totals in each time period
remain the same, as the number people born in each region cannot increase or decrease. Second,
differences in cells must implicitly be driven solely by migration. These migrations occurs by
individuals changing their place of residence (moving across columns), whilst their place of birth
(row) characteristic remains fixed.
To derive a corresponding set of flows that are constrained to meet the stocks tables, we can
alternatively consider the top panel of a Table 1 as a set of R birth place specific migration flow
tables where the marginal totals are known, shown in the bottom panel of Table 1. These are
formed by considering each row of the two consecutive stock tables as a set of separate margins
of a migration flow table. Place of residence totals at time t from the stock data now become
origin margin (row) totals for each birth place specific population. Similarly, place of residence
totals at time t + 1 from the stock data now become destination margin (column) totals for
each birth place specific population. As the row totals from the stock tables were equal, the
row and column margins in each of the birth place specific migration flow tables in Table 1 are
also equal.
The cells in each of these tables may be considered as missing data from a log-linear model,
which are commonly used in the estimation of migration flow tables when only marginal totals
are known, see for example Willekens (1999). Estimates for the missing non-diagonal cells,
constrained to match the marginal totals in the bottom panel of Table 1 can the be obtained by
fitting a quasi-independent log-linear model using the contained maximization routine outlined
in Abel (2012). Rather than being estimated, diagonal cells representing non-movers, are fixed
to their maximum values possible, given the known marginal totals. As a result estimated flows
represent the minimum number of migration transition flows required to meet the corresponding
stock tables.
The full estimates of both the fixed diagonal cells, and the estimated non-diagonal cells
are shown in the top panel of Table 2. Summing over all birth place dimensions and deleting
non-movers in the diagonal elements allows us to obtain a traditional flow table of migrant
transitions from origin i to destination j during the time period t to t + 1 in the bottom panel
of Table 2.
In reality, natural changes from births and deaths in the population occur, causing differences
in the row totals between subsequent years. In addition, members of the population that are
alive in both time periods, may have not been recorded in one or both periods. These problems
can be controlled for using standard demographic accounting methods (see Abel (2012) for more
details).
3
Table 2: Estimates of Migrant Transition Flow Tables Based on Stock Data in Table 1, with
Known Diagonals
Estimates of Origin-Destination-Place of Birth Flow Tables:
950
0
0
0
950
0
100
0
0
100
50
0
10
0
60
Origin
Place of Birth=C
Destination
A
B
C
A
B
C
D
Sum
80
10
0
0
90
0
30
0
0
30
0
0
800
0
800
Sum
0
0
0
0
0
1000
100
10
0
1110
D
Sum
0
0
0
40
40
80
40
800
40
960
A
B
C
D
Sum
55
555
50
5
665
Place of Birth=D
Destination
A
B C
D
Sum
A
B
C
D
Sum
55
25
0
0
80
20
0
10
10
40
0
505
0
0
505
0
25
10
10
45
0
25
50
0
75
Sum
0
0
0
5
5
Origin
A
B
C
D
Sum
D
Place of Birth=B
Destination
A
B C
D
Origin
Origin
Place of Birth=A
Destination
A
B
C
0
0
0
0
0
0
0
0
180
180
20
25
20
200
265
Estimates of Total Origin-Destination Flow Table:
Origin
A
4
A
B
C
D
Sum
Destination
B
C
D
0
35
10
10
55
10
10
20
50
25
0
75
0
0
0
0
Sum
50
60
20
20
150
Results
Place of birth data, published by the World Bank were used to provide foreign born migration
stock tables at the start of each the last five decade. Using this data, the conditional maximisation routine was run to calculate four 191 × 191 tables of global migrant transition flows over
10-year periods (see http://goo.gl/B29lD for a spreadsheet of these estimates). Due to the
large amount of data estimated, some form of dimension reduction is required in order to asses
estimates. In this paper, the estimated migrant transition flows from the applied methodology
are discussed with reference to comparable estimates of global net migration flows of the United
4
Nations and models for international migration.
Figure 1: Scatter Plot of Estimated Net 10-year Migrant Transition Flow Rates vs Derived UN
Rates (per 000)
1960s
1970s
QAT
ARE
●
●
KWT 1000
●
500
ARE
●
MAC
●
GUF
●
BHS
NCL
●
● CIV
DJI
BRN
●
ISR
TKM
●
●
AUS
GLP
PYF
CHE
MYS
BEL
BHR
SAU
●
LUX
●
KAZ
PSE
●
GMB
ARM
FRA
LBY
S
WE
GAB
●
●
CAN
TJK
DEU
TGO
SWZ
●
●
●
●
NZL
ZWE
●
MDA
KGZ
EST
UZB
●
MTQ
UKR
BTN
LVA
●
SYR
SLE
LBR
●
PNG
UGA
GUY
LKA
●●
GBR
COD
NOR
ABW
ALB
BRB
●
USA
VEN
●
CMR
DNK
ZAF
●
MDV
TLS
COG
REU
KHM
NAM
ISL
●
MWI
CHN
ETH
IND
TZA
DOM
BGD
KOR
MNG
JPN
BRA
BWA
NGA
S
TWN
LB
PER
VNM
●
LAO
HUN
VUT
IRN
TTO
SDN
MMR
●
ECU
●
IDN
STP
THA
NER
NIC
AFG
BOL
MRT
GTM
PRK
●
KEN
SVK
MDG
●
SLV
BGR
POL
TCD
COM
GHA
EGY
●
TON
SVN
AUT
PHL
COL
●●
●●
●
NPL
ZMB
CHL
●
TUR
OMN
ROU
SOM
ARG
RUS
MEX
IRQ
CZE
SGP
●
CRI
LTU
PAN
MOZ
LCA
FJI
CAF
ITA
AGO
●
●
●
SEN
●
HTI
BEN
NLD
URY
ESP
●●MUS
CPV
FSM
●
PRY
●
PAK
GIN
HND
YEM
● ●
●
AZE
MLI
FIN
●●
●
BDI
●
●
HRV
●
●
●
LBN
LSO
RWA
●
●
VCT
GRD
●
MKD
●
●
●
●
●
●
●
●●
GRC
●
0
BLR
●
●MAR
●
●
SUR
●
ERI
●●
●
● ●GNB
●
●
SCG
BIH
●
●
●
ANT
●
CYP
●
TUN
●
GNQ
●
●
●
●
●
JAM
CUB
●
WSM
●
●
●
●
● PRT
●
GEO
●
●
JOR
●
BFA
●
●●
●
●
●
●
● PRI
● MLT
DZA
●
●
●●
●
●
● ●●
IRL
BLZ
QAT
●
500
HKG
●
KWT
●
BHR
SAU
●
●
GUF
BRN
●
CIV
DJI
COM
LBY
●
OMN
EST
LUX
GAB
GMB
IRL
LVA
●
●
●●
AUS
PAK
LTU
DEU
●●
BEL
VEN
COG
CAN
●●
USA
MDA
SWE
NOR
●
●
NGA
ISR
ESP
●
FRA
MWI
NLD
CAF
UZB
UKR
●
ITA
BLZ
KGZ
SLE
SVN
CRI
TKM
SVK
CHE
PYF
YEM
MOZ
HRV
●
ARM
ALB
ZAF
AUT
●
NCL
SLB
SOM
GBR
RWA
GNB
KEN
IND
FIN
TZA
MNG
HUN
SDN
CZE
ETH
BIH
MDV
KAZ
BGR
KOR
JPN
BTN
DNK
THA
BRA
GRC
IRN
TLS
CHN
TJK
BLR
RUS
ROU
IDN
MMR
PER
PRY
HND
●
NAM
BOL
●
ZWE
ECU
TWN
●
●
DZA
ZMB
ARG
GTM
VNM
PNG
●
MDG
TCD
MYS
CMR
COD
NPL
●
SYR
VUT
SGP
●
NER
POL
●
TGO
UGA
MRT
SCG
LBR
IRQ
NZL
AGO
NIC
COL
SEN
DOM
PHL
EGY
CHL
●●
ISL
●
AFG
●
●
HTI
PRK
●
SLV
●
●
SWZ
ERI
MEX
●●
●
●
●
●
●
●
●
●
MAR
FJI
CUB
●
BFA
●
BDI
●
LAO
●
GEO
●
●
●
●
TUN
AZE
BWA
GIN
●●
MLT
●
URY
●
STP
LSO
●
●
●
KHM
PRT
TUR
PAN
●
0
LKA
MLI
●
●
●
●
LCA
PRI
●●
TTO
●
GHA
●●
●
CYP
●
●
●
●
ABW
MUS
●
●
●
●
●
BEN
●
●BGD
●
●
●
●●
●
●●
●
●
●JAM
●
●
GNQ CPV
●
●
ANT
BHS
LBN
●
●●
JOR
REU
PSE
●
FSM
VCT
●●●
GUY
BRB
● ●
● ●●
TON
SUR
MKD
●
●
●
●
●
●
WSM
GLP
GRD
●
MTQ
Estimated Net Rate per 000
●
●
HKG
●
−500
MAC
●
●
−200
0
200
1980s
400
600
●
●
−500
0
500
1990s
1000
QAT
●
ARE
●
400
ARE
●
400
GUF
QATMAC
ABW
●
●
●
GUF
BRN ● SGP
●
●
PSELUX
ISR
● ●
●
BHR
BLZ
MLT
RWA
●
● GAB
HKG
●
● ● CYP
DJI
PRT
USA
OMN
LSO
GMBBWA
●
CHE
CAN
CRI
●
SAU
ESP
CIV
●
●
●
● ●
DEU
RUS
GRC
MUS
AUS
● GBR
AUT
DNK
SWE
●
NLD
NAM
●
●●
ITA
GIN AFG
●
BTN
● LBY
SWZ
NOR
●●LBN
NZL
●
KHM
●
JOR
BLR
●
BEL
KEN
ROU
●
TWN
●
MDV
●●
FRA
●
JPN
ISL
TCD
MYS
SYR
MOZ
●
SLB
PRK
MWI
COM
DZA
CHL
●
MDG
NGA
ZMB
YEM
●
THA
ETH
●
MNG
NPL
FIN
CHN
●
HUN
TZA
IND
●
SVN
PAN
CMR
BGD
BRA
●● ●
SDN
NER
MRT
PNG
ARG
VEN
MMR
●●
GHA
IDN
●
TUN
MAR
SEN
IRN
UKR
CAF
ZWE
COD
●
PAK
●
NCL
●
EGY
TUR
ZAF
BDI
AGO
BEN
URY
VNM
KOR
PYF
IRL
●
UGA
GNB
●
LKA
TLS
●
●
●
BHS
●
PHL
●
●
●
●
HRV
BGR
●
COL
LAO
●
●
BOL
●
●BRB
TGO
VUT
●
PER
●●
●
●
●
●
●
0
●
MLI
SVK
CUB
CZE
●
●
TKM
●
PRY
●
●
●IRQ
●
PRI
SLEERI
●GTM
●
BFA
●
●
●● ●
●●
● SOM
●
●
●
●
●
●
●
●
ECU
●●
●
UZB
SCG
COG
HND
HTI
●
POL
●
●
●
●
●
●
FSM
●
MDA
NIC
●DOM
●● ● ●● GNQ
●
●
●MKD
●
●
● TJK
● MEX
●
●
LBR
● ● REU
SUR
FJI
SLV
●
AZE
●
TTO
LCA
●
LTU
●●KGZ
●●
●
LVA
●● ●
KAZ EST
GEO
●
●
JAM
GRD
STP
●
ARM●
● ●
● WSM
GUY CPV
●
VCT
KWT BIH ● ● ●
●
●
TON
−200
● ANT
MTQ
●
ALB ●
●
GLP
●
200
0
AFG
●
−200
200
GLP KWT
SAU
●●
MTQ●
●
BHR
●
REU
●OMN
MKD LUX
●
DJI
●
● SGP
●
PYF
GAB
MAC
GMB
CZE
CHE
●
AUS
LVA
●
SVN
●
●●
NCL
FSM
LBY
NLD
HRV
●
EST
CAN
●CIV
USA
●
●
AUT
ISR
●
ABW BEN
●
●NOR
GRC
●
SWE
CRI
●●●
COG
●
●
LTU
DNK
●
BWA
ITA
●
DEU
TGO
ESP
NER
FRA
NPL
MWI
●
BTN
CMR
MYS
PRT
● IRN
CAF
BDI
BEL
RUS
●
GBR
ZAF
TLS
NAM
VEN
FIN
HKG
PRY
●
JPN
MNG
ETH
TUR
SLB
MMR
GHA
CHN
UKR
KEN
HUN
BLR
PAK
MDG
●
●
TZA
IND
THA
BRA
SDN
PNG
MDV
●
●
SEN
RWA
MRT
PRK
SYR
IDN
●
COD
SLE
POL
BGD
DZA
UGA
ARG
NGA
CHL
AGO
●
ECU
●
PER
TCD
●
TWN
●
●
VNM
ZWE
●
UZB
ISL
BFA
GNQ
MOZ
KOR
●
COL
BOL
●
GIN
●
●
TUN
NZL
●
MDA
KHM
ZMB
●●
●
PAN
HND
SVK
SOMLBR
●
YEM
● ● ●
IRQ
VUT
●
●
EGY
●
●
LBN
PSE
PHL
●
●
●
URY
●
CUB
●
●
TJK
MAR
●
●
●
MLT
BHS
●
ERI
● ●MLI
●
GTM
●
LKA
●●
SWZ
●
●
●
DOM
●
●
●
●
MEX
BGR
SCG
●
KAZ
●
●
●
●●
●
●
●
●GNB
HTI
●
●
COM
IRL
●
●
●
●
●
LAO
TKM
●
●
MUS
●
●●JOR
●
AZE
ALB
●
●
●
GEO
ROU
●
●
●
ARM
●
KGZ
●
●
●
NIC
●●
●
●CYP
STP
●
FJI
●
●
●
PRI
●● ●
LSO
●BRB
SLV
●
TTO
●
CPV
LCA
● ● ●BIH
VCT
GRD
● JAM
●
TON ●● ●● ANT
● BRN
●
WSM
BLZ ●
●
●
GUY
●
SUR
●
−400
−200
0
200
●
400
−300
−200
−100
0
100
200
300
United Nations Net Rate per 000
A scatter plot comparing the estimated net rates (on the y-axis) with the derived United
Nations (United Nations Population Division, 2011) net rate (x-axis) is shown in Figure 1 from
each decade. Noticeable is the general linear trend along the x = y line, indicating a broad
conformity of the estimated with the derived UN net rates. Noticeable is the general linear
trend along the x = y line, indicating a broad conformity of the estimated with the derived UN
net rates. This pattern is confirmed in separate regressions for each decade of the estimated
rate on the derived United Nations rate and an intercept, discussed in more detail, alongside
5
possible explanations for the outlier values in Abel (2012).
Kim and Cohen (2010) investigated non-economic predictors of reported international migration bilateral flows. Using a log normal regression model, geographic, demographic and social
and historical determinants were estimated twice, once using migration flow data by origin into
17 destinations countries and using migration flow data by destinations from 13 origin countries from data provided by the United Nations United Nations Population Division (2009).
Comparison of the parameter values from the same model fitted to the estimated flows and the
parameter values from Kim and Cohen (2010) show some broad similarities in direction and size.
Discrepancies (fully explored in Abel (2012)) are predominately explained by the march larger
pool of flow data available from the estimates, which additionally includes migration between
traditionally less developed countries.
5
Conclusion
Comparable international migration flow data are needed by researchers to better understand
people’s movements and identify patterns. Policy makers can also use comparable international
migration flow data to help forecast populations better, where migration can often play an
important role. The methodology outlined in this paper provides a relatively simple yet powerful
technique to estimate global migration flow tables, exploiting newly available global stock data.
References
Abel, G. J. (2010). Estimation of international migration flow tables in Europe. Journal of the
Royal Statistical Society: Series A (Statistics in Society) 173 (4), 797–825.
Abel, G. J. (2012). Estimating Global Migration Flow Tables Using Place of Birth. Vienna
Institute of Demography Working Paper (01/2012).
Beer, J., J. Raymer, R. van der Erf, and L. van Wissen (2010, November). Overcoming the
Problems of Inconsistent International Migration data: A New Method Applied to Flows in
Europe. European Journal of Population/Revue europ´eenne de D´emographie 26 (4), 459–481.
Kim, K. and J. E. Cohen (2010). Determinants of International Migration Flows to and from
Industrialized Countries: A Panel Data Approach Beyond Gravity1. International Migration
Review 44 (4), 899–932.
Nowok, B., D. Kupiszewska, and M. Poulain (2006). Statistics on International Migration Flows.
In M. Poulain, N. Perrin, and A. Singleton (Eds.), Towards the Harmonisation of European
Statistics on International Migration (THESIM), Chapter 8, pp. 203–233. Louvain-La-Neuve,
Belguim: UCL–Presses Universitaires de Louvain.
¨
Ozden,
c., C. R. Parsons, M. Schiff, and T. L. Walmsley (2011, March). Where on Earth is
Everybody? The Evolution of Global Bilateral Migration 19602000. World Bank Economic
Review , 12–56.
6
Raymer, J., G. J. Abel, and P. W. F. Smith (2007, October). Combining census and registration
data to estimate detailed elderly migration flows in England and Wales. Journal of the Royal
Statistical Society: Series A (Statistics in Society) 170 (4), 891–908.
Raymer, J., J. J. Forster, P. W. F. Smith, J. Bijak, and A. Wi´sniowski (2012). Integrated Modelling of European Migration: Background, Specification and Results. NORFACE Migration
Discussion Paper (4).
United Nations Population Division (2009). International Migration Flows to and from Selected
Countries: The 2008 Revision.
United Nations Population Division (2011). World Population Prospects: The 2010 Revision,
Highlights and Advance Tables. Working Paper ESA/P/WP (220).
Willekens, F. (1999). Modeling Approaches to the Indirect Estimation of Migration Flows:
From Entropy to EM. Mathematical Population Studies 7 (3), 239–78.
7