Transcript Accounting for Migration and Remittance Effects

Accounting for Migration and
Remittance Effects
Susan Pozo
Prepared for Conference on Regional Trade Agreements,
Migration and Remittances with Special Focus on CAFTA and
Latin America
Sam Houston State University
April 12, 2008
Much more attention paid to the
migratory process in the past 5 years
1. Is this a research fad?
Econ Lit Hits
140
120
100
80
60
40
20
0
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Source: Econ Lit database, 2008
2. Growth in the number of persons affected by
the migratory process?
Source: U.S. Bureau of the Census, 2008
Percent Foreign Born
United States
1850
1860
1870
1880
1890
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
9.7
13.2
14.4
13.3
14.8
13.6
14.7
13.2
11.6
8.8
6.9
5.4
4.7
6.2
7.9
10.4
0
5
10
15
World Migrants
100
0
50
Migrants
150
200
(in millions)
1960
1965
1970
Source: Data from UN (2008)
1975
1980
1985
1990
1995
2000
2005
World Migrant Stock
0
1
2
3
(percent of world population)
1960
1965
1970
Source: Data from UN (2008)
1975
1980
1985
1990
1995
2000
2005
Remittances to Mexico
(quarterly frequency, in millions of US dollars)
7000
6000
5000
4000
3000
2000
1000
0
1996
1998
2000
2002
2004
REMITTANCES
Source: Data from Banco Central de Mexico, 2008
2006
Remittances to Mexico
(yearly frequency, Percent of GDP)
.030
.025
.020
.015
.010
.005
.000
1980
1985
1990
1995
WR_GDP
Source: World Development Indicators, 2008
2000
2005
Remittances to Italy as a percent of Italian GDP
(1880-1910)
7
6
5
4
3
2
1
80
85
90
95
00
05
10
15
20
PERCENT
Source: Computed by the author with data from Cinel (1991) and from
Flandreau & Zumer (2004)
1990
3. Increased dispersion of the
foreign born?
2006
Source: US Census Bureau, http://factfinder.census.gov
Percent population foreign born
.15
0
.05
.1
Density
.2
.25
.3
1990
0
5
10
15
p1990
Computed by the author from Census Bureau
20
25
30
Percent population foreign born
.15
.1
0
.05
Density
.2
.25
.3
2000
0
5
10
15
p2000
Computed by the author from Census Bureau
20
25
30
Percent population foreign born
.15
.1
0
.05
Density
.2
.25
.3
2006
0
5
10
15
p2006
Computed by the author from Census Bureau
20
25
30
Percent population foreign born
.15
.1
.05
0
0
5
10
15
p1990
20
25
30
Percent population foreign born
.2
.15
.1
0
.05
Increased spread of the
foreign-born in 2006
relative to 1990
.25
.3
2006
Density
Density
.2
.25
.3
1990
0
5
10
15
p2006
20
25
30
3. Increased dispersion of the foreign-born?
30
25
20
15
10
5
0
1990
2000
2006
Source: Computed by author from 1990, 2000 Decennial Censuses and 2006 American
Community Survey, US Census.
Economic Development Effects of the
Migratory Process on
Labor
supply
Health
Education
Happiness
Poverty
levels
Business
Investments
Tend to focus on only one facet of the
migratory process…
Poverty -- remittances
Labor force participation – remittances
Education—remittances
Business Investment—(return) migration
Health – emigration
Happiness - migration
Migratory
Process
Remittances
Migration
Economic Development Effects of the
Migratory Process on
Labor
supply
Health
Education
Happiness
Poverty
levels
Business
Investments
Migrant HH and Remittance Receipt
Haiti
17%
41%
neither
migrant only
remit only
38%
4%
Source: Amuedo-Dorantes, Georges and Pozo, (2007)
R&M
Remit
migrant
neither
14
Mexico
4
9
74
12
10
DR
13
65
25
Haiti
33
3.5
38
Source: Computed by author from LAMP and MMP databases
HH type in %
Cuenca, Ecuador
M&R
Remit
Migrant
Neither
18%
Too large
14%
Too small
64%
4%
Computed by the author from : Discrimination and Economic Outcomes Survey
Database, IADB, 2006
We miss out on the story when we
focus on one or the other alone
In the modeling of education a typical strategy
might be to estimate:
Education = βRemit +δX +Є
Several problems:
i) endogeneity due to reverse causality
ii) endogeneity due to omitted variable bias
Type of Household
Model Specification
Variables
Remittance Receipt
HH Currently Employed
Assets
% dependent age
Ed 17+
Ed female adult
% kids school age
Own Child
Boy
Child’s Age
Firstborn Child
Urban
No. of Observations
Wald Chi2-test
Prob>Chi2
Log pseudolikelihood
Source: Amuedo-Dorantes, Georges and Pozo, (2007)
All
Probit
M.E.
.0067
-0.0199
0.0494***
0.3121
-0.2857*
0.0979
-0.3581**
0.1090*
-0.0210
0.0075
-0.0326**
-0.1263
327
23.71
0.0222
-104.4399
Typical solution
Instrument for remittances:
Using migration or variables linked to longstanding migratory patterns, such as the
mapping of railroads. Essentially migration
networks.
Problems with this Approach…
1. An instrument can’t be something that should be in the
equation in the first place, i.e. migration and variables
proxying for long-standing migratory patterns are likely to
impact educational attainment via:
 A disruptive effect, in the case of family migration
 A network effect, in the case of both family and broadly
defined migration networks
Education
Migratory
Process
Remittances
Migration
Migration
K/networks
Everything
else
Migration capital/networks
Expected value of additional education varies
with the probability of future migration
EVH = (pH) RH,H + ( 1 - pH) RH,US
Type of Household
Model Specification
Migration networks/capital
Household Head Currently Employed
Current Household Assets
Percent of Non-working Age Household Members
Mean Potential Education of 17 Years +
Potential Ed Attainment of Spouse or Head
Percent of School-age Children in the HH
Own Child
Boy
Child’s Age
Firstborn Child
All
Probit
Coefficient
0.4827**
0.0037
0.2743***
1.8011
-1.7777**
0.3882
-2.1341***
0.4865
-0.1973*
0.0196
-0.1239
Problems with this Approach…
1. An instrument can’t be something that should be in the
equation in the first place, i.e. migration and variables
proxying for long-standing migratory patterns are likely to
impact educational attainment via:
 A disruptive effect, in the case of family migration
 A network effect, in the case of both family and broadly
defined migration networks
2. We notice significant differences in selectivity with
respect to different types of HHs. HHs without migrants
receiving remittances are very different from HHs with
migrants receiving remittances.
Conclusions
1. Redesign of surveys to take into account the
diversity in the incidence of migration and
remittances.
2. Redesign of econometric methodology to
recognize differential “migration,”
“remittance” and “migration capital” effects.
Type of Household
Model Specification
Variables
Remittance Receipt
HH Currently Employed
Assets
% dependent age
Ed 17+
Ed female adult
% kids school age
Own Child
Boy
Child’s Age
Firstborn Child
Urban
No. of Observations
Wald Chi2-test
Prob>Chi2
Log pseudolikelihood
IV Exogeneity Testa
Wald Test of Exogeneity
All
Non-migrant
Probit
M.E.
.0067
-0.0199
0.0494***
0.3121
-0.2857*
0.0979
-0.3581**
0.1090*
-0.0210
0.0075
-0.0326**
-0.1263
327
23.71
0.0222
-104.4399
n.a.
n.a.
IV-Probit
M.E.
0.6791***
-0.2073*
0.0213
0.0223
0.0182
-0.2607
-0.2329
0.1594**
0.0214
-0.0067
0.0402
0.0216
258
1181.35
0.0000
-243.2202
0 < = 5.99
Chi2(1)=19.85
Prob>Chi2=0.0000
Source: Amuedo-Dorantes, Georges and Pozo, (2007)
Sources: Population Division of the Department of Economic and Social
Affairs of the United Nations Secretariat, Trends in Total Migrant Stock:
The 2005 Revision http://esa.un.org/migration, Saturday, April 05, 2008;
8:31:39 AM.
Marc Flandreau and Frédréric Zumer, The Making of Global Finance, 18801913, OECD 2004. (Italian GDP data)
Cinel, Dino, “The national integration of Italian return migration, 1870-1929.
Cambridge, Cambridge University Press, 1991.