On the Determinants of Global Bilateral Migration Flows
Jesus Crespo Cuaresma∗+ , Mathias Moser∗ , Anna Raggl∗+
∗
+
WU - Vienna University of Economics and Business
WiC - Wittgenstein Centre for Demography and Global Human Capital
Antwerp, June 27th, 2013
1 / 26
Introduction
Introduction
Little is known about the determinants of global bilateral migration
flows, as no complete database exists
We aim at assessing the determinants of global bilateral migration
flows
I
I
We use the fact that net migration flows are the difference between
aggregated immigration and aggregated emigration flows
We construct a statistical model that relates the determinants of
bilateral flows to net migration flows
As common in the literature, an (extended) gravity model is used to
model the (non-extisting) migration flows
2 / 26
Gravity
The Gravity Model I
Relates the flows of goods or factors between two countries to their
attractive mass and to the distance between them
Origins in Ravenstein (1885, 1889)
I
I
“. . . the bulk of migrants ought to travel short distances only . . . ”
“. . . increase in the means of locomotion and a development of
manufactures and commerce have led to an increase of migration . . . ”
Tinbergen (1962): first application to model trade flows between 2
countries i and j, tij
GDPi GDPj
tij = f
dij
where GDPi GDPj represents the gravitational mass and dij the
distance between the two countries
3 / 26
Gravity
The Gravity Model II
Most general form for application to migration flows (Vanderkamp,
1977)
αPOPiβ1 POPjβ2
mij =
dijβ3
in logs
log mij = α + β1 log POPi + β2 log POPj + β3 log dij
In most applications, income per capita, common language, colonial
history and other covariates are included in the estimation
4 / 26
Literature
Literature - Determinants of Migration Flows/Stocks I
Gravity models for limited samples
Gravity model for immigration to US (Clark, Hatton, and Williamson,
2007)
Assessment of bilateral migration flows to US and Canada (Karemera,
Oguledo, and Davis, 2000)
Determinants of flows to OECD countries (Pedersen, Pytlikova, and
Smith, 2008; Kim and Cohen, 2010; Mayda, 2010; Beine, Docquier,
and Ozden, 2011; Ortega and Peri, 2013)
Determinants of stocks in OECD countries (Peri, 2005; Grogger and
Hanson, 2011)
5 / 26
Literature
Literature - Determinants of Migration Flows/Stocks II
Lessons learned:
Standard gravity variables have expected effects
Policy changes influence level and source country composition of
migrants
Income relative to education
Political stability and individual freedom in source countries increase
immigration to US
Network effects of particular importance
Non-linearities in source country income
Evidence for positive selection and positive sorting
6 / 26
Literature
Literature - Determinants of Net Migration I
Explaining net migration rates (Hatton and Williamson, 2003b,a)
Age distribution appears to matter
Wage differentials are important, negative effect of GDP growth in
home country on emigration rates
Stock of migrants increases immigration
Reaction to civil wars visible
Poverty constraint
7 / 26
Literature
Literature
“South-south” migration is ignored in all previous papers
Direction of migration (% of global migrant stock)
Definition
S-S
N-S
N-N
S-N
Development Status
Income Level
UNDP HDI
33%
42%
45%
7%
4%
4%
26%
16%
14%
34%
39%
37%
Table: Proportion of global migration across “South” and “North” (Bakewell, 2009)
8 / 26
Literature
Literature - Estimating Bilateral Migration Flows
Abel (2013) derives bilateral migration flows between 191 countries from
sequential stock tables
Bayesian methods based on Poisson specifications
Sparsely specified models, based on distance
9 / 26
Methodology
From Bilateral Flows to Net Migration: Specification I
Assume that (log) bilateral migration flows between country i and country
j are given by
mij = log Mij = Xij β + uij
(1)
Bilateral flows are not observed, but data exist on net migration (Ni ),
which is given by the difference of migration flows to country i from all
other countries and migration out of country i to all other countries,
X
X
X
X
Ni = Mi∗ − M∗i =
Mij −
Mji =
exp mij −
exp mji . (2)
j6=i
j6=i
j6=i
j6=i
10 / 26
Methodology
From Bilateral Flows to Net Migration: Specification II
In matrix form,
N = f (m) = (In ⊗ ιn ) exp [Xβ + u] − B exp [Xβ + u] = S exp [Xβ + u] ,
{z
}
|
{z
}
|
sum of immigrants
sum of emigrants
(3)
where S = (In ⊗ ιn ) − B
Approximating S exp [Xβ + u] by S exp [Xβ] + u, (pseudo-)maximum
likelihood estimation can be used to estimate the parameters of the model
11 / 26
Results
Results - Simulation I
A Monte Carlo exercise
Simulate bilateral matrices Xij and add white noise for different levels
of e.g. R 2
Generate the (unknown) bilateral migration flows Mij from a model
and calculate net migration flows Ni
Estimate M using N and X as regressors by Maximum Likelihood
Simulation of 3 regressors + intercept, 100 countries and 6 levels of
noise
y = α + 0.1X1 + 0.5X2 − 0.5X3 + u
where u ∼ N(0, σ 2 ) and X ∼ N(0, 1) for different values of σ 2
corresponding to alternative noise-to-signal ratios
12 / 26
Results
Results - Simulation II
Table: ML results for simulated migration data, 1000 replicates
R2 =
0.95
0.90
0.85
0.80
0.75
0.70
β0 (1.0)
RMSE
Mean
0.082
1.01
0.121
1.02
0.156
1.02
0.215
1.04
8.922
0.65
12.748
0.11
β1 (0.1)
RMSE
Mean
0.017
0.10
0.027
0.10
0.036
0.10
0.041
0.10
0.083
0.10
0.970
0.06
β2 (−0.5)
RMSE
Mean
0.027
-0.50
0.039
-0.50
0.050
-0.50
0.067
-0.51
0.863
-0.55
1.970
-0.63
β3 (0.5)
RMSE
Mean
0.026
0.50
0.039
0.50
0.050
0.51
0.066
0.51
1.659
0.57
2.180
0.64
13 / 26
Results
Results - Empirics I
Net migration for 172 countries (29929 obs)
Covariates
I
I
I
log per capita GDP and log population of source and destination
countries
distance, common border, colonial history and common language
dummies
migration stocks in destination countries
Origin and destination dummies for 21 regions
Estimated using Maximum Likelihood
14 / 26
Results
Results - Empirics II
ln(distance)
× Origin North
× Origin South
ln(gdp pc destination)
× Origin North
× Origin South
ln(gdp pc origin)
× Origin North
× Origin South
ln(pop destination)
× Origin North
× Origin South
ln(pop origin)
× Origin North
× Origin South
contiguity
colony
× Origin North
× Origin South
common language
share migration stock
× Origin North
× Origin South
South Origin
log likelihood
-0.7271∗∗∗
0.4335∗∗∗
∗∗∗
-0.3332
0.6433
∗∗∗
0.5544∗∗∗
∗∗∗
1.1478
2.6209∗∗∗
∗∗∗
0.3484
0.0969∗∗∗
-144381.1
[0.0765]
[0.0922]
[0.0399]
[0.0443]
[0.0307]
[0.2325]
[0.1309]
[0.0652]
[0.0023]
-0.3809∗∗∗
-0.6495∗∗∗
[0.0520]
[0.0465]
1.5736∗∗∗
0.6241∗∗∗
[0.2583]
[0.0718]
0.6749∗∗∗
-0.0414
[0.1166]
[0.0771]
1.0799∗∗∗
0.8178∗∗∗
[0.0962]
[0.0457]
0.7322∗∗∗
0.6451∗∗∗
1.7603∗∗∗
[0.0618]
[0.0232]
[0.1493]
3.5571∗∗∗
0.8475∗∗∗
0.2949∗∗∗
[0.2153]
[0.1670]
[0.0984]
0.0040
0.0950∗∗∗
0.8562∗∗∗
[0.0115]
[0.0027]
[0.2995]
-137685.2821
15 / 26
Results
Results - Empirics III
(1)
ln(distance)
× Origin North
× Origin South
× North-North
× North-South
× South-North
× South-South
ln(gdp pc destination)
× Origin North
× Origin South
× North-North
× North-South
× South-North
× South-South
ln(gdp pc × Origin)
× Origin North
× Origin South
× North-North
× North-South
× South-North
× South-South
...
-0.7271∗∗∗
0.4335∗∗∗
-0.3332∗∗∗
(2)
[0.0765]
[0.0922]
[0.0399]
-0.3809∗∗∗
-0.6495∗∗∗
1.5736∗∗∗
0.6241∗∗∗
0.6749∗∗∗
-0.0414
(3)
[0.0520]
[0.0465]
[0.2583]
[0.0718]
[0.1166]
[0.0771]
-0.5469∗∗∗
-0.2397∗∗
-0.7564∗∗∗
-0.6625∗∗∗
[0.0916]
[0.1150]
[0.0883]
[0.0950]
0.7552∗∗∗
-1.6009∗∗∗
0.4820∗∗
1.7540∗∗∗
[0.1653]
[0.2442]
[0.2014]
[0.3417]
1.1179∗∗∗
-5.6341∗∗∗
-0.3907∗∗
-0.1738
[0.1427]
[1.2347]
[0.1588]
[0.3713]
16 / 26
Results
Results - Empirics IV
(1)
...
contiguity
colony
× Origin North
× Origin South
× North-North
× North-South
× South-North
× South-South
common language
share migration stock
× Origin North
× Origin South
× North-North
× North-South
× South-North
× South-South
1.1478∗∗∗
2.6209∗∗∗
0.3484∗∗∗
0.0969∗∗∗
(2)
[0.2325]
[0.1309]
[0.0652]
[0.0023]
(3)
1.7603∗∗∗
[0.1493]
3.5571∗∗∗
0.8475∗∗∗
[0.2153]
[0.1670]
0.2949∗∗∗
[0.0984]
0.0040
0.0950∗∗∗
[0.0115]
[0.0027]
1.2658∗∗∗
[0.2550]
3.5113∗∗∗
-11.6176
2.8741∗∗∗
0.1576
0.3125∗
[0.2718]
[610912]
[0.2608]
[0.4067]
[0.1808]
0.0307∗∗∗
0.0356
0.1000∗∗∗
-0.3835
[0.0078]
[0.0916]
[0.0055]
[0.2967]
17 / 26
Results
600
800
Results - Empirics V
predicted net migration
400
200
USA
ESP
0
-200
ITA
RUS
THA
CAN
GBR
FRA
ARE
DEU
AUS
ZAF
PRT
MYS
TUR
HKG
GRC
SGP
SVN
BDI
HRV
MDV
NZL
TMP
BWA
IRN
BTN
NLD
AUT
SAU
BIH
ROM
HUN
BEL
MKD
LSO
ALB
SWZ
JPN
OMN
COM
ISR
MLT
LBR
LBY
SWE
BLR
SLE
EST
KOR
LVA
KWT
CHE
QAT
GUY
DNK
LTU
NOR
LBN
IRQ
BHR
SYR
BGR
DJI
CYP
YEM
ARM
JOR
IRL
MNG
ISL
S
SUR
UR
GEO
FIN
SVK
VUT
SLB
TON
LUX
COG
FJI
ARG
CPV
GMB
GNB
GNQ
MUS
VCT
GRD
CAF
GAB
RWA
LCA
AGO
ERI
WSM
CMR
MDA
BLZ
CZE
FSM
TCD
SLV
MWI
MRT
ECU
TGO
PNG
BRB
ZAR
LKA
MDG
CRI
BEN
JAM
GIN
MOZ
HTI
TTO
PRY
ZMB
MLI
CHL
SEN
BHS
AZE
URY
BOL
NAM
NER
CIV
BFA
VEN
GHA
U
UGA
GA
BRN
KEN
TZA
HND
LAO
DOM
GTM
POL
TUN
ETH
PAN
DZA
KAZ
UKR
COL
NGA
KGZ
TJK
EGY
NIC
TKM
TKM
PER
KHM
SDN
NPL
BRA
VNM
MAR
UZB
MMR
PHL
IDN
BGD
MAC
CHN
PAK
IND
MEX
-200
0
200
400
actual net migration
600
800
18 / 26
Results
400
Results - Empirics VI
predicted net migration
-200
0
200
ESP
ITA
RUS
MAC
THA
CAN
GBR
FRA
ARE
DEU
AUS
ZAF
PRT
MYS
TUR
HKG
GRC
SGP
SVN
BDISLE
HRV
MDV
NZL
TMP
BWA
IRN
BTN
NLD
AUT
BIH
ROM
HUN
BEL
MKD
LSO
ALB
SWZ
JPN
OMN
ISR
COM
MLT
LBR
LBY
SWE
BLR
EST
KOR
LVA
KWT
CHE
QAT
GUY
DNK
LTU
LBN
NOR
IRQ
BHR
BGR
DJI
CYP
YEM
JOR
ARM
IRL
MNG
ISL
SUR
GEO
FIN
SVK
VUT
SLB
TON
LUX
COG
FJI
ARG
CPV
GMB
GNB
GNQ
MUS
VCT
GRD
CAF
GAB
RWA
LCA
AGO
ERISYR
WSM
CMR
MDA
BLZ
CZE
FSM
TCD
SLV
MWI
MRT
ECU
TGO
PNG
BRB
ZAR
LKA
MDG
CRI
BEN
JAM
GIN
MOZ
TTO
HTI
PRY
ZMB
MLI
CHL
SEN
BHS
AZE
URY
BOL
NER
NAM
CIV
BFA
VEN
GHA
UGA
BRN
KEN
TZA
HND
LAO
DOM
GTM
POL
TUN
ETH
PAN
DZA
KAZ
UKR
COL
NGA
KGZ
TJK
EGY
NIC
TKM
PER
KHM
SDN
NPL
BRA
VNM
MAR
UZB
MMR
PHL
BGDIDN
CHN
SAU
PAK
IND
-400
MEX
-400
-200
0
actual net migration
200
400
19 / 26
Projections
Projecting Migration Flows I
Lutz and KC (2013) and Crespo Cuaresma (2013) provide internally
consistent projections of population and GDP for 150 countries up to
the year 2100
We use median scenario projections to obtain estimates of the
migration flows for the coming decades
How is the distribution of migration flows to Europe expected to
change?
20 / 26
Projections
1
Projecting Migration Flows II
-.5
Change in immigration to EU
0
.5
BRN
KWT
BHR
ISR
SAU
NER BFA
TGO BEN
GAB OMN
MDG
UGA
TZA
SEN
MWI
IRL
DJI
LBY
CIV
BHS
TCD COM
USA
GNB MRT
SGP
CAF GHA
CAN
MYS
COG
CYPAUS
GNQ
MUS
CMR
MDV
PRY
CRI
SYR
NZL
SLB
BRB
RWA
NOR
SWZ
TUR
BDI LBR
HKG
MLI
PAK
GMB
JOR
GTM
DZA
FIN
HNDBOL
ETHERI GIN
PAN
BLZ
VENMEX
ZMBKEN
COL
NGA
PHL
CHE
IRQ
EGY
DNK
LUX
DEU
CHL TTO
ISL
GRD
BWA
AUT
NPLLSOSDN NIC LKAVUT ECU ZAF
SLE
LCA
NLD
KOR ESP
ARG
IRN
FRA
JPN
MAR
TUNBRA
GBR
DOM
MOZ
BGD
MLT
SVN
PER LBN
PRT SWE
SLV
BEL
NAM
URY
MNG
EST
ITA
CPV
VCT
LAO
CZE
YEMBTN FJI
PNG
HTI
GRC
MKD
JAM
HUN
GUY TON THA
HRV
KAZ
SUR
LTU SVK
BGR
RUS
LVA
IND
ROM
ZAR
IDN
BLR
KHM VNM
POL
TKMWSM
BIH
AZE UKR
UZB
CHN
KGZ ARM
GEO
TJK
MDA
6
7
8
9
log(GDP per capita) in 2000
10
11
21 / 26
Projections
Projecting Migration Flows III
The expected demographic and economic long-term developments imply,
ceteris paribus,
an increase of migration flows to Europe in the coming decades
a significant relative increase of migration flows from lower income
countries
a stabilization of international migration flows within European
economies
22 / 26
Conclusions
Conclusions
On a global level: no bilateral migration flows available
Bilateral migration data can be reconstructed from net migration
using a non-linear model
Extended gravity model applies using distance, GDP per capita,
population, common language and borders, colonial history
I
I
I
Distance seems to be greatest obstacle for migrants originating in the
south
GDP per capita in destination country acts as important pull-factor,
effect highest for south-north migration
Network effects visible, in particular for south-north migration
Projections of immigration flows to the EU suggest that overall
immigration flow will increase
23 / 26
References
References I
Abel, G. J. (2013): “Estimating Global Migration Flow Tables Using Place of Birth
Data,” Demographic Research, 28(18), 505–546.
Bakewell, O. (2009): “South-South Migration and Human Development: Reflections
on African Experiences,” Human Development Reports Research Paper 2009/07,
United Nations Development Programme.
Beine, M., F. Docquier, and C. Ozden (2011): “Diasporas,” Journal of
Development Economics, 95(1), 30–41.
Clark, X., T. J. Hatton, and J. G. Williamson (2007): “Explaining U.S.
Immigration, 1971-1998,” The Review of Economics and Statistics, 89(2), 359–373.
Grogger, J., and G. H. Hanson (2011): “Income maximization and the selection
and sorting of international migrants,” Journal of Development Economics, 95(1),
42–57.
Hatton, T. J., and J. G. Williamson (2003a): “Demographic and Economic
Pressure on Emigration out of Africa*,” Scandinavian Journal of Economics, 105(3),
465–486.
24 / 26
References
References II
(2003b): “What Fundamentals Drive World Migration?,” Working Papers
UNU-WIDER Research Paper 23, World Institute for Development Economic
Research (UNU-WIDER).
Karemera, D., V. I. Oguledo, and B. Davis (2000): “A gravity model analysis of
international migration to North America,” Applied Economics, 32(13), 1745–1755.
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.
Mayda, A. M. (2010): “International migration: a panel data analysis of the
determinants of bilateral flows,” Journal of Population Economics, 23, 1249–1274.
Ortega, F., and G. Peri (2013): “The Effect of Income and Immigration Policies
on International Migration,” Migration Studies, 1(1), 47–74.
Pedersen, P. J., M. Pytlikova, and N. Smith (2008): “Selection and Network
Effects. Migration flows into OECD countries 1990–2000,” European Economic
Review, 52(7), 1160 – 1186.
25 / 26
References
References III
Peri, G. (2005): “International Migrations: Some Comparisons and Lessons for the
European Union,” Working Papers 06-36, University of California at Davis,
Department of Economics.
Ravenstein, E. G. (1885): “The Laws of Migration,” Journal of the Statistical
Society of London, 48(2), pp. 167–235.
(1889): “The Laws of Migration,” Journal of the Royal Statistical Society,
52(2), pp. 241–305.
Tinbergen, J. (1962): Shaping the World Economy; Suggestions for an International
Economic Policy. Twentieth Century Fund, New York.
Vanderkamp, J. (1977): “The Gravity Model and Migration Behaviour: An Economic
Interpretation,” Journal of Economic Studies, 4(2), 89–102.
26 / 26